RecSysWiki - User contributions [en]

RankSys

2016-02-14T11:43:08Z

Saulvargas:

'''RankSys''' is a new [[framework]] for the implementation and [[evaluation]] of [[recommendation algorithms]] and techniques that has resulted from a line of research work that is currently documented in several [https://github.com/RankSys/RankSys/wiki/References publications] and a [http://saulvargas.es/phd-thesis.pdf PhD thesis]. While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of [[novelty]] and [[diversity]]. RankSys derives its name from explicitly targeting the [[ranking]] task problem, rather than [[rating prediction]]. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8, which is the most recent version of the popular programming language. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the [https://www.mozilla.org/en-US/MPL/2.0/ MPL 2.0].

The publicly available version of this framework (0.4.2) includes implementations of several collaborative filtering recommendation algorithms, a wide variety of novelty and diversity metrics and re-ranking techniques and state-of-the-art compression techniques for in-memory collaborative filtering data. If you want to know more, check the [https://github.com/RankSys/RankSys GitHub site] or the [https://github.com/RankSys/RankSys/wiki wiki], which provides a high-level description of the different components of the current release of the software. Finally, [https://twitter.com/RankSys follow us in Twitter] to keep in touch!

== External links ==
* Webpage: http://ranksys.org/
* GitHub repository: https://github.com/RankSys/RankSys
* Wiki: http://github.com/RankSys/RankSys/wiki
* Javadoc: http://ranksys.org/javadoc/
* Twitter: https://twitter.com/ranksys

[[Category: Java]]
[[Category: Software]]

Recommendation Software

2016-01-18T09:59:23Z

Saulvargas:

Recommendations are generally provided through a self-implemented software, a machine learning package or a specific '''recommendation-focused''' software package or library.

The table below shows a comparison of some of the more common software packages for recommendation.







{| class="wikitable sortable"
|-
! Package !! Language !! Inception year !! Status !! Source !! License
|-
| [[LensKit]] || Java || 2011 || Active || [https://github.com/lenskit/lenskit GitHub] || LGPLv2.1+
|-
| [[Mrec]] || Python || 2013 || Stale || [https://github.com/mendeley/mrec GitHub] || NA
|-
| [[MyMediaLite]] || C# || 2011 || Active || [https://github.com/zenogantner/MyMediaLite GitHub] || GPL
|-
| [[Python-recsys]] || Python || 2011 || Stale || [https://github.com/ocelma/python-recsys GitHub] || NA
|-
| [[RankSys]] || Java || 2015 || Active || [https://github.com/RankSys/RankSys GitHub] || MPL
|-
| [[RecDB]] || PostgreSQL || 2013 || Active || [https://github.com/Sarwat/recdb-postgresql GitHub] || BSD
|-
| [[Recommender101]] || Java || 2013 || Active || [http://ls13-www.cs.tu-dortmund.de/homepage/recommender101/download/recommender101.zip source zip] || custom
|-
| [[TagRec]] || Java || 2014 || Active || [https://github.com/learning-layers/TagRec GitHub] || GPL

|}

===See also===
* [[Recommendation Datasets]]

[[Category:Software]]

RankSys

2015-10-13T16:32:29Z

Saulvargas:

'''RankSys''' is a new [[framework]] for the implementation and [[evaluation]] of [[recommendation algorithms]] and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ranksys.github.io/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of [[novelty]] and [[diversity]]. RankSys derives its name from explicitly targeting the [[ranking]] task problem, rather than [[rating prediction]]. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

The publicly available version of this framework (v0.3) includes implementations of several collaborative filtering recommendation algorithms as well as a wide variety of novelty and diversity metrics and re-ranking techniques. The modules published to date are the following:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-fast, which provides support for fast and efficient implementation of data structures and algorithms.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-rec, which provides support for generating recommendation lists.
* RankSys-nn, which implements nearest neighbors recommendation algorithms.
* RankSys-mf, which implements matrix factorization recommendation algorithms.
* RankSys-novdiv, which provides common resources for novelty and diversity metrics and enhancement techniques.
* RankSys-novelty, which contains novelty metrics and enhancement techniques
* RankSys-diversity, which contains diversity metrics and enhancement techniques.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://github.com/RankSys/RankSys/wiki wiki of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/RankSys/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ranksys.github.io/
* GitHub repository: https://github.com/RankSys/RankSys
* Wiki: http://github.com/RankSys/RankSys/wiki

[[Category: Java]]
[[Category: Software]]

Recommendation Software

2015-10-13T16:30:08Z

Saulvargas:

Recommendations are generally provided through a self-implemented software, a machine learning package or a specific '''recommendation-focused''' software package or library.

The table below shows a comparison of some of the more common software packages for recommendation.







{| class="wikitable sortable"
|-
! Package !! Language !! Inception year !! Status !! Source !! License
|-
| [[LensKit]] || Java || 2011 || Active || [https://github.com/lenskit/lenskit GitHub] || LGPLv2.1+
|-
| [[Mrec]] || Python || 2013 || Stale || [https://github.com/mendeley/mrec GitHub] || NA
|-
| [[MyMediaLite]] || C# || 2011 || Active || [https://github.com/zenogantner/MyMediaLite GitHub] || GPL
|-
| [[Python-recsys]] || Python || 2011 || Stale || [https://github.com/ocelma/python-recsys GitHub] || NA
|-
| [[RankSys]] || Java || 2015 || Active || [https://github.com/RankSys/RankSys GitHub] || GPL
|-
| [[RecDB]] || PostgreSQL || 2013 || Active || [https://github.com/Sarwat/recdb-postgresql GitHub] || BSD
|-
| [[Recommender101]] || Java || 2013 || Active || [http://ls13-www.cs.tu-dortmund.de/homepage/recommender101/download/recommender101.zip source zip] || custom
|-
| [[TagRec]] || Java || 2014 || Active || [https://github.com/learning-layers/TagRec GitHub] || GPL

|}

===See also===
* [[Recommendation Datasets]]

[[Category:Software]]

List of recommender system dissertations

2015-08-05T09:03:33Z

Saulvargas: /* 2015 */

[[Recommender systems]] related '''doctoral''' dissertations by year. For other types of theses see:
* [[List of recommender systems master's theses]]
* [[List of recommender systems bachelor's theses]]

Dissertation within a year are sorted alphabetically by title.

=== 2015 ===
* [http://krex.k-state.edu/dspace/bitstream/handle/2097/19779/RohitParimi2015.pdf?sequence=6&isAllowed=y Collaborative Filtering Approaches for Single-domains and Cross-domain Recommender Systems] - [[Rohit Parimi]]

* [http://ir.ii.uam.es/saul/saulvargas-thesis.pdf Novelty and Diversity Evaluation and Enhancement in Recommender Systems] - [[Saúl Vargas]]

* [http://docear.org/papers/Towards%20Effective%20Research-Paper%20Recommender%20Systems%20and%20User%20Modeling%20based%20on%20Mind%20Maps.pdf Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps] - Joeran Beel

=== 2014 ===
* [http://scholarlyrepository.miami.edu/cgi/viewcontent.cgi?article=2209&context=oa_dissertations A Model-Based Music Recommendation System for Individual Users and Implicit User Groups] - [[Yajie Hu]]
* [http://infoscience.epfl.ch/record/199806 Aggregating Information from the Crowd: ratings, recommendations and predictions] - [[Florent Garcin]]
* [http://wanlab.poly.edu/xiwang/doc/thesis.pdf Collaborative Filtering Based Social Recommender Systems] - [[Xiwang Yang]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/3158723 Cross-domain Recommendations based on semantically-enhanced User Web Behavior] - [[Julia Hoxha]]
* [http://www.win.tue.nl/ipa/?event=cryptographically-enhanced-privacy-for-recommender-systems Cryptographically-Enhanced Privacy for Recommender Systems] - [[Arjan Jeckmans]]
* [http://conservancy.umn.edu/handle/11299/167084 Database management system support for collaborative filtering recommender systems] - [[Mohamed Sarwat]]
* [http://bit.ly/simonphd Dynamic Generation of Personalized Hybrid Recommender Systems] - [[Simon Dooms]]
* Enhancing Discovery in Geoportals: Geo-Enrichment, Semantic Enhancement and Recommendation Strategies for Geo-Information Discovery - [[Bernhard Vockner]]
* [http://www.lsi.upc.edu/~vcodina/phd.pdf Exploiting Distributional Semantics for Content-Based and Context-Aware Recommendation] - [[Victor Codina]]
* [http://micheletrevisiol.com/data/Exploiting_Implicit_User_Activity_for_Media_Recommendation.pdf Exploiting Implicit User Activity for Media Recommendation] - [[Michele Trevisiol]]
* [http://arks.princeton.edu/ark:/88435/dsp01nv935299q Information Aggregation in Quantized Consensus, Recommender Systems, and Ranking] - [[Shang Shang]]
* [http://repository.lib.ncsu.edu/ir/bitstream/1840.16/9727/1/etd.pdf More Usable Recommendation Systems for Improving Software Quality] - [[Yoonki Song]]
* [http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2550&context=etd Next Generation of Recommender Systems: Algorithms and Applications] - [[Lei Li]]
* [http://ethesis.unifr.ch/theses/downloads.php?file=TeranL.pdf SmartParticipation: A Fuzzy-Based Recommender System for Political Community-Building] - [[Luis Fernando Terán Tamayo]]
* [http://elehack.net/research/thesis/ Towards Recommender Engineering: Tools and Experiments for Identifying Recommender Differences] - [[Michael Ekstrand]]
* [http://tampub.uta.fi/bitstream/handle/10024/95965/978-951-44-9551-9.pdf User Factors in Recommender Systems: Case Studies in e-Commerce, News Recommending, and e-Learning] - [[Juha Leino]]

=== 2013 ===
* [http://interactivesystems.info/publications/a-conceptual-model-and-a-software-framework-for-developing-context-aware-hybrid-recommender-systems A conceptual model and a software framework for developing context aware hybrid recommender systems] - [[Tim Hussein]]
* [http://shodhganga.inflibnet.ac.in/handle/10603/24399 Effective tag recommendation system based on topic ontology] - [[V Subramaniyaswamy]]
* [http://eprints.ucm.es/24533/ Estrategias de recomendación basadas en conocimiento para la localización personalizada de recursos en repositorios educativos] (Spanish) - [[Almudena Ruiz-Iniesta]]
* [http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/3681 Evaluating the Accuracy and Utility of Recommender Systems] - [[Alan Said]]
* [http://julian-urbano.info/publications/061-evaluation-audio-music-similarity Evaluation in Audio Music Similarity] - [[Julián Urbano]]
* [https://biblio.ugent.be/input/download?func=downloadFile&recordOId=4163727&fileOId=4163752 Improved online services by personalized recommendations and optimal quality of experience parameters] - [[Toon De Pessemier]]
* [http://hdl.handle.net/10059/859 Integrating Content and Semantic Representations for Music Recommendation] - [[Ben Horsburgh]]
* [http://escholarship.org/uc/item/4xw874p5#page-1 Latent feature models for dyadic prediction] - [[Aditya Krishna Menon]]
* [http://edok01.tib.uni-hannover.de/edoks/e01dh13/767033973.pdf Living Analytics Methods for the Social Web] - [[Ernesto Diaz-Aviles]]
* [http://repository.tudelft.nl/assets/uuid:f7d3977e-f191-40d4-8f27-784a32902a55/thesis_yueshi.pdf Ranking and Context-awareness in Recommender Systems] - [[Yue Shi]]
* [https://repositorio.uam.es/bitstream/handle/10486/14091/66095_campos%20soto%20pedro%20g..pdf Recommender Systems and Time Context: Characterization of a Robust Evaluation Protocol to Increase Reliability of Measured Improvemenets] - [[Pedro Campos]]
* [http://users.soe.ucsc.edu/~jwang30/index.files/dissertation-Jian.pdf Session Aware Recommender System in E-Commerce] - [[Jian Wang]]
* [http://doras.dcu.ie/17737/ Social contextuality and conversational recommender systems] - [[Eoin Hurrell]]
* [http://liris.cnrs.fr/Documents/Liris-6406.pdf Trace-Based Reasoning for User Assistance and Recommendations] - [[Raafat Zarka]]
* [http://kth.diva-portal.org/smash/record.jsf?pid=diva2:606503 Trust-Based User Profiling] - [[Nima Dokoohaki]]
* [http://scidok.sulb.uni-saarland.de/volltexte/2013/5528/pdf/Boehmer_2013_MobileApplicationUsage_low.pdf Understanding and supporting mobile application usage] - [[Matthias Böhmer]]
* [http://d-scholarship.pitt.edu/19733/ User Contrallability in a Hybrid Recommender System] - [[Denis Parra]]

=== 2012 ===
* [http://library.epfl.ch/en/theses/?nr=5318 Design and User Perception Issues for Personality-Engaged Recommender Systems] - [[Rong Hu]]
* [http://www.di.uniba.it/~swap/papers/musto_thesis.pdf Enhanced Vector Space Models for Content-based Recommender Systems] - [[Cataldo Musto]]
* [http://dalspace.library.dal.ca/bitstream/handle/10222/14735/Lipczak,%20Marek,%20PhD,%20CS,%20March%202012.pdf?sequence=5 Hybrid Tag Recommendation in Collaborative Tagging Systems] - [[Marek Lipczak]]
* [http://gradworks.umi.com/35/05/3505275.html Interaction Methods for Large Scale Graph Visualization Systems --- Using Manipulation to Aid Discovery] - [[Brynjar Gretarsson]]
* [http://www.evazangerle.at/wp-content/papercite-data/pdf/evaphd.pdf Leveraging Recommender Systems for the Creation and Maintenance of Structure within Collaborative Social Media Platforms] - [[Eva Zangerle]]
* [https://eldorado.tu-dortmund.de/bitstream/2003/29661/1/Dissertation.pdf Leveraging Tagging Data for Recommender Systems] - [[Fatih Gedikli]]
* [http://cseweb.ucsd.edu/~bmcfee/papers/bmcfee_dissertation.pdf More like this: machine learning approaches to music similarity] - [[Brian McFee]]
* [http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-17249 On some Challenges for Online Trust and Reputation Systems] - [[Mozhgan Tavakolifard]]
* [http://www.l3s.de/~krestel/cmsimple3_2/?Publications:Others:PhD_Thesis_12 On the Use of Language Models and Topic Models in the Web: New Algorithms for Filtering, Classification, Ranking, and Recommendation] - [[Ralf Krestel]]
* [http://ir.ii.uam.es/~alejandro/thesis/thesis-bellogin.pdf Performance prediction and evaluation in Recommender Systems: an Information Retrieval perspective] - [[Alejandro Bellogin]]
* [http://eprints.qut.edu.au/59507/1/Noraswaliza_Abdullah_Thesis.pdf Integrating collaborative filtering and matching-based search for product recommendation] - [[Noraswaliza Abdullah]]
* [http://www.uns.ac.rs/sr/doktorske/aleksandraKlasnjaMilicevic/disertacija.pdf Personalized Recommendation Based on Collaborative Tagging Techniques for an E-learning System] - [[Aleksandra Klašnja‐Milićević]]
* [http://arxiv.org/pdf/1203.4487v2 Recommender systems in industrial contexts] - [[Frank Meyer]]
* [http://opus.bsz-bw.de/ubhi/volltexte/2012/167/pdf/item_recommendation.pdf Supervised Machine Learning Methods for Item Recommendation] - [[Zeno Gantner]]
* [http://gradworks.umi.com/35/23/3523081.html Understanding Consistency of Recommender Systems: Behavioral and Algorithmic Perspectives] - [[Jingjing Zhang]]

=== 2011 ===
* [http://users.cecs.anu.edu.au/~sguo/thesis.pdf Bayesian Recommender Systems: Models and Algorithms] - [[Shengbo Guo]]
* [http://www.inf.unibz.it/~lbaltrunas/doc/linas_phd_thesis.pdf Context-Aware Collaborative Filtering Recommender Systems] - [[Linas Baltrunas]]
* [http://benfields.net/bfields_thesis.pdf Contextualize Your Listening: The Playlist as Recommendation Engine] - [[Ben Fields]]
* [http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED534217&ERICExtSearch_SearchType_0=no&accno=ED534217 Deployment of Recommender Systems: Operational and Strategic Issues] - [[Abhijeet Ghoshal]]
* [http://espace.library.uq.edu.au/view/UQ:263213 Effective and Efficient Collaborative Filtering] - [[Yi Ding]]
* [http://www.cse.cuhk.edu.hk/lyu/_media/students/thesisxin9.pdf Effective Fusion-based Approaches for Recommender Systems] - [[Xin Xin]]
* [http://www.aka-verlag.com/de/detail?ean=978-3-89838-332-5 Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems] - [[Robert Jäschke]]
* [http://uclab.khu.ac.kr/resources/thesis/PhD_Thesis_Weiwei.pdf Improved Trust-Aware Recommender System using Small-Worldness of Trust Networks] - [[Weiwei Yuan]]
* [http://conservancy.umn.edu/handle/117321 Personalized Recommendation in Social Network Sites] - [[Jilin Chen]]
* [http://lac-repo-live7.is.ed.ac.uk/bitstream/1842/5770/1/Givon2011.pdf Predicting and using social tags to improve the accuracy and transparency of recommender systems] - [[Sharon Givon]]
* [http://slavnik.fe.uni-lj.si/markot/uploads/Main/2010_tkalcic_phd.pdf Recognition and usage of emotive parameters in recommender systems] - [[Marko Tkalčič]]
* [http://gradworks.umi.com/34/79/3479962.html Swarm intellilgence for clustering dynamic data sets for web usage mining and personalization] - [[Esin Saka]]
* [http://eprints.qut.edu.au/41879/1/Huizhi_Liang_Thesis.pdf User profiling based on folksonomy information in Web 2.0 for personalized recommender systems] - [[Huizhi Liang]]
* [http://eprints.qut.edu.au/49168/1/Touhid_Bhuiyan_Thesis.pdf Trust-based automated recommendation making] - [[Touhid Bhuiyan]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/1687272 Using Data Mining for Facilitating User Contributions in the Social Semantic Web] - [[Maryam Ramezani]]
* [http://gradworks.umi.com/34/49/3449549.html Visualization of music relational information sources for analysis, navigation, and discovery] - [[Justin Donaldson]]

=== 2010 ===
* [http://www.amazon.com/Domain-Independent-Framework-Intelligent-Recommendations-Application/dp/3838113756 A Domain-Independent Framework for Intelligent Recommendations] - [[Jörn David]]
* [http://www.cp.jku.at/people/seyerlehner/supervised/seyerlehner_phd.pdf Content-Based Music Recommender Systems: Beyond simple Frame-Level Audio Similarity] - [[Klaus Seyerlehner]]
* [http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-16897-0 Context-Aware Ranking with Factorization Models] - [[Steffen Rendle]]
* [http://www.cs.ucl.ac.uk/staff/n.lathia/thesis.html Evaluating Collaborative Filtering Over Time] - [[Neal Lathia]]
* [http://opus.kobv.de/tuberlin/volltexte/2010/2695/pdf/wetzker_robert.pdf Graph-Based Recommendation in Broad Folksonomies] - [[Robert Wetzker]]
* [http://lib.tkk.fi/Diss/2010/isbn9789526031514/isbn9789526031514.pdf Methods and Applications for Ontology-based Recommender Systems] - [[Tuukka Ruotsalo]]
* [http://theses.gla.ac.uk/2132/01/2010hopfgartner1phd.pdf Personalised video retrieval: application of implicit feedback and semantic user profiles] - [[Frank Hopfgartner]]
* [http://www.dsc.ufcg.edu.br/~lbmarinho/homepage/pub/thesis_marinho.pdf Recommender Systems for Social Tagging Systems] - [[Leandro Balby Marinho]]
* [http://www.verlagdrkovac.de/3-8300-5081-X.htm Recommender Systeme für produktbegleitende Dienstleistungen] - [[Margarethe Frohs]]
* [http://wrap.warwick.ac.uk/3759/1/WRAP_THESIS_Li_2010.pdf Relational clustering models for knowledge discovery and recommender systems] - [[Tao Li]]
* [https://biblio.ugent.be/publication/986279/file/1886805.pdf Trust networks for recommender systems] - [[Patricia Victor]]
* [http://gradworks.umi.com/34/70/3470158.html User session and history modeling for collaborative visualization] - [[Fanhai Yang]]

=== 2009 ===
* [http://opus.kobv.de/tuberlin/volltexte/2009/2245/pdf/lommatzsch_andreas.pdf Eine offene Architektur für die agentenbasierte, adaptive, personalisierte Informationsfilterung] (German) - [[Andreas Lommatzsch]]
* [http://www.abdn.ac.uk/~csc284/Nava%20Tintarev_PhD_Thesis_%282010%29.pdf Explaining recommendations] - [[Nava Tintarev]]
* [http://www.omikk.bme.hu/collections/phd/Villamosmernoki_es_Informatikai_Kar/2010/Pilaszy_Istvan/ertekezes.pdf Factorization-Based Large Scale Recommendation Algorithms] - [[István Pilászy]]
* [http://www.shilad.com/shilads_thesis.pdf Nurturing Tagging Communities] - [[Shilad Sen]]
* [http://repository.upenn.edu/dissertations/AAI3363295/ Recommender systems and market diversity] - [[Daniel M. Fleder]]
* [http://itlab.dbit.dk/~toine/?page_id=6 Recommender Systems for Social Bookmarking] - [[Toine Bogers]]
* [http://svn.egovmon.no/svn/phdgoodwin/thesis/referenceexample/MMR_thesis_afterDefense.pdf Towards Efficient Music Similarity Search, Ranking, and Recommendation] - [[Maria Magdalena Ruxanda]]

=== 2008 ===
* [http://essay.utwente.nl/59711/1/MA_thesis_J_de_Wit.pdf Evaluating recommender systems : an evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context] - [[Joost de Wit]]
* [http://arantxa.ii.uam.es/~cantador/doc/2008/thesis08.zip Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach] - [[Iván Cantador]]
* [http://eprints.qut.edu.au/29165/2/Li-Tung_Weng_Thesis.pdf Information Enrichment for Quality Recommender Systems] - [[Li-Tung Weng]]
* [http://tomheath.com/thesis/html Information-seeking on the Web with Trusted Social Networks – from Theory to Systems] - [[Tom Heath]]
* [http://www.cs.umass.edu/~marlin/research/phd_thesis/marlin-phd-thesis.pdf Missing Data Problems in Machine Learning] - [[Benjamin Marlin]]
* [http://mtg.upf.edu/node/1217 Music Recommendation and Discovery in the Long Tail] - [[Òscar Celma]]
* [http://hal.inria.fr/docs/00/34/83/70/PDF/TeseFinal.pdf Recommender System based on Personality Traits] - [[Maria Augusta Silveira Netto Nunes]]
* [http://winnie.kuis.kyoto-u.ac.jp/members/yoshii/d-thesis-yoshii.pdf Studies on Hybrid Music Recommendation Using Timbral and Rhythmic Features] - [[Kazuyoshi Yoshii]]
* [http://www.peterlang.com/index.cfm?event=cmp.ccc.seitenstruktur.detailseiten&seitentyp=produkt&pk=56341&cid=367 Towards Metadata-aware Algorithms for Recommender Systems] - [[Karen H. L. Tso-Sutter]]
* [http://hci.epfl.ch/members/lichen/EPFL_TH4140.pdf User Decision Improvement and Trust Building in Product Recommender Systems] - [[Li Chen]]

=== 2007 ===
* [http://www.ulrichpaquet.com/Papers/PhDThesis.pdf Bayesian Inference for Latent Variable Models] - [[Ulrich Paquet]]
* [http://www-users.cs.umn.edu/~arashid/thesis.pdf Mining Influence in Recommender Systems] - [[Al Mamunur Rashid]]
* [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]

=== 2006 ===
* [http://dl.acm.org/citation.cfm?id=1269509 Designing social interactions with animated avatars and speech output for product recommendation agents in electronic commerce] - [[Lingyun Qiu]]
* [http://www-users.cs.umn.edu/~mcnee/mcnee-thesis-preprint.pdf Meeting User Information Needs in Recommender Systems] - [[Sean McNee]]

=== 2005 ===
* [http://eprints.ecs.soton.ac.uk/10692/1/wei-phd-thesis.pdf A Market-Based Approach to Recommender Systems] - [[Yan Zheng Wei]]
* [http://dl.acm.org/citation.cfm?id=1123874 Explanet: a learning tool and hybrid recommender system for student-authored explanations] - [[Jessica Masters]]
* Hybrid recommendation techniques based on user profiles - [[Pasquale Lops]]
* [http://www.princeton.edu/~smorris/pdfs/PhD/Ozmen.pdf Information Transmission and Recommender Systems] - [[Deran Özmen]]
* [https://doc.telin.nl/dsweb/Get/Document-56873 Supporting People In Finding Information: Hybrid Recommender Systems and Goal-Based Structuring] - [[Mark van Setten]]
* [http://www.freidok.uni-freiburg.de/volltexte/1804/pdf/Thesis.pdf Towards Decentralized Recommender Systems] - [[Cai-Nicolas Ziegler]]
* [http://dspace.mit.edu/bitstream/handle/1721.1/31137/61184336.pdf?sequence=1 Use of Discrete Choice Models with Recommender Systems] - [[Bassam H. Chaptini]]

=== 2004 ===

=== 2003 ===
* [http://eia.udg.es/~mmontane/montaner-thesis03.pdf Collaborative recommender agents based on case-based reasoning and trust] - [[Miquel Montaner]]
* [https://circle.ubc.ca/bitstream/handle/2429/14933/ubc_2003-859525.pdf?sequence=1 The Impact of Internalization and Familiarity on Trust and Adoption of Recommendation Agents] - [[Sherrie Komiak]]
* [http://knuth.luther.edu/~bmiller/Papers/thesis.pdf Toward a Personal Recommender System] - [[Bradley N. Miller]]

=== 2002 ===
* [http://dl.acm.org/citation.cfm?id=935978 Recommendation as classification and recommendation as matching: two information-centered approaches to recommendation] - [[Chumki Basu]]

=== 2001 ===
* [http://www.patrickbaudisch.com/publications/2001-Baudisch-Dissertation-DynamicInformationFiltering.pdf Dynamic Information Filtering] - [[Patrick Baudisch]]
* [http://www.cs.uni.edu/~schafer/publications/schafer_thesis.pdf MetaLens: A Framework for Multsource Recommendations] - [[Ben Schafer]]
* [http://www-users.cs.umn.edu/~sarwar/thesis.ps Sparsity, scalability, and distribution in recommender systems] - [[Badrul Munir Sarwar]]

=== 2000 ===

=== 1999 ===

=== 1998 ===

=== 1997 ===
* Recommender Systems for Problem Solving Environments - [[Naren Ramakrishnan]]

== External links ==
* [http://pampalk.at/mir-phds/ PhD Theses and Doctoral Dissertations Related to Music Information Retrieval]
* [http://www.mendeley.com/groups/3058301/recommender-system-dissertations/papers/ Mendeley collection of Recommender System dissertations] (based on this page)

[[Category: List|Dissertation]]

Recommendation Software

2015-08-05T08:52:56Z

Saulvargas:

RankSys

2015-05-18T22:07:03Z

Saulvargas:

RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ir-uam.github.io/RankSys/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

The publicly available version of this framework (v0.3) includes implementations of several collaborative filtering recommendation algorithms as well as a wide variety of novelty and diversity metrics and re-ranking techniques. The modules published to date are the following:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-fast, which provides support for fast and efficient implementation of data structures and algorithms.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-rec, which provides support for generating recommendation lists.
* RankSys-nn, which implements nearest neighbors recommendation algorithms.
* RankSys-mf, which implements matrix factorization recommendation algorithms.
* RankSys-novdiv, which provides common resources for novelty and diversity metrics and enhancement techniques.
* RankSys-novelty, which contains novelty metrics and enhancement techniques
* RankSys-diversity, which contains diversity metrics and enhancement techniques.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://github.com/ir-uam/RankSys/wiki wiki of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/ir-uam/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ir-uam.github.io/RankSys/
* GitHub repository: https://github.com/ir-uam/RankSys
* Wiki: http://github.com/ir-uam/RankSys/wiki

[[Category: Java]]
[[Category: Software]]

RankSys

2015-05-18T20:23:15Z

Saulvargas:

RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ir-uam.github.io/RankSys/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

The publicly available version of this framework (v0.3) includes implementations of several collaborative filtering recommendation algorithms as well as wide variety of novelty and diversity metrics and re-ranking techniques. The modules published to date are the following:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-fast, which provides support for fast and efficient implementation of data structures and algorithms.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-rec, which provides support for generating recommendation lists.
* RankSys-nn, which implements nearest neighbors recommendation algorithms.
* RankSys-mf, which implements matrix factorization recommendation algorithms.
* RankSys-novdiv, which provides common resources for novelty and diversity metrics and enhancement techniques.
* RankSys-novelty, which contains novelty metrics and enhancement techniques
* RankSys-diversity, which contains diversity metrics and enhancement techniques.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://github.com/ir-uam/RankSys/wiki wiki of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/ir-uam/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ir-uam.github.io/RankSys/
* GitHub repository: https://github.com/ir-uam/RankSys
* Wiki: http://github.com/ir-uam/RankSys/wiki

[[Category: Java]]
[[Category: Software]]

List of recommender system dissertations

2015-03-27T15:13:38Z

Saulvargas: /* 2013 */

[[Recommender systems]] related dissertations by year.

Dissertation within a year are sorted alphabetically by title.

=== 2015 ===
* [http://ir.ii.uam.es/saul/saulvargas-thesis.pdf Novelty and Diversity Evaluation and Enhancement in Recommender Systems] - [[Saúl Vargas]]

=== 2014 ===
* [http://scholarlyrepository.miami.edu/cgi/viewcontent.cgi?article=2209&context=oa_dissertations A Model-Based Music Recommendation System for Individual Users and Implicit User Groups] - [[Yajie Hu]]
* [http://infoscience.epfl.ch/record/199806 Aggregating Information from the Crowd: ratings, recommendations and predictions] - [[Florent Garcin]]
* [http://wanlab.poly.edu/xiwang/doc/thesis.pdf Collaborative Filtering Based Social Recommender Systems] - [[Xiwang Yang]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/3158723 Cross-domain Recommendations based on semantically-enhanced User Web Behavior] - [[Julia Hoxha]]
* [http://www.win.tue.nl/ipa/?event=cryptographically-enhanced-privacy-for-recommender-systems Cryptographically-Enhanced Privacy for Recommender Systems] - [[Arjan Jeckmans]]
* [http://conservancy.umn.edu/handle/11299/167084 Database management system support for collaborative filtering recommender systems] - [[Mohamed Sarwat]]
* [http://bit.ly/simonphd Dynamic Generation of Personalized Hybrid Recommender Systems] - [[Simon Dooms]]
* Enhancing Discovery in Geoportals: Geo-Enrichment, Semantic Enhancement and Recommendation Strategies for Geo-Information Discovery - [[Bernhard Vockner]]
* [https://dl.dropboxusercontent.com/u/13894587/hcorona-msc-2014.pdf Enhancing Content Discovery. An Evaluation and Classification Perspective] - [[Humberto Corona]]
* [http://www.lsi.upc.edu/~vcodina/phd.pdf Exploiting Distributional Semantics for Content-Based and Context-Aware Recommendation] - [[Victor Codina]]
* [http://micheletrevisiol.com/data/Exploiting_Implicit_User_Activity_for_Media_Recommendation.pdf Exploiting Implicit User Activity for Media Recommendation] - [[Michele Trevisiol]]
* [http://arks.princeton.edu/ark:/88435/dsp01nv935299q Information Aggregation in Quantized Consensus, Recommender Systems, and Ranking] - [[Shang Shang]]
* [http://repository.lib.ncsu.edu/ir/bitstream/1840.16/9727/1/etd.pdf More Usable Recommendation Systems for Improving Software Quality] - [[Yoonki Song]]
* [http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2550&context=etd Next Generation of Recommender Systems: Algorithms and Applications] - [[Lei Li]]
* [http://ethesis.unifr.ch/theses/downloads.php?file=TeranL.pdf SmartParticipation: A Fuzzy-Based Recommender System for Political Community-Building] - [[Luis Fernando Terán Tamayo]]
* [http://elehack.net/research/thesis/ Towards Recommender Engineering: Tools and Experiments for Identifying Recommender Differences] - [[Michael Ekstrand]]
* [http://tampub.uta.fi/bitstream/handle/10024/95965/978-951-44-9551-9.pdf User Factors in Recommender Systems: Case Studies in e-Commerce, News Recommending, and e-Learning] - [[Juha Leino]]

=== 2013 ===
* [http://interactivesystems.info/publications/a-conceptual-model-and-a-software-framework-for-developing-context-aware-hybrid-recommender-systems A conceptual model and a software framework for developing context aware hybrid recommender systems] - [[Tim Hussein]]
* [http://shodhganga.inflibnet.ac.in/handle/10603/24399 Effective tag recommendation system based on topic ontology] - [[V Subramaniyaswamy]]
* [http://eprints.ucm.es/24533/ Estrategias de recomendación basadas en conocimiento para la localización personalizada de recursos en repositorios educativos] (Spanish) - [[Almudena Ruiz-Iniesta]]
* [http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/3681 Evaluating the Accuracy and Utility of Recommender Systems] - [[Alan Said]]
* [https://biblio.ugent.be/input/download?func=downloadFile&recordOId=4163727&fileOId=4163752 Improved online services by personalized recommendations and optimal quality of experience parameters] - [[Toon De Pessemier]]
* [http://hdl.handle.net/10059/859 Integrating Content and Semantic Representations for Music Recommendation] - [[Ben Horsburgh]]
* [http://escholarship.org/uc/item/4xw874p5#page-1 Latent feature models for dyadic prediction] - [[Aditya Krishna Menon]]
* [http://repository.tudelft.nl/assets/uuid:f7d3977e-f191-40d4-8f27-784a32902a55/thesis_yueshi.pdf Ranking and Context-awareness in Recommender Systems] - [[Yue Shi]]
* [https://repositorio.uam.es/bitstream/handle/10486/14091/66095_campos%20soto%20pedro%20g..pdf Recommender Systems and Time Context: Characterization of a Robust Evaluation Protocol to Increase Reliability of Measured Improvemenets] - [[Pedro Campos]]
* [http://users.soe.ucsc.edu/~jwang30/index.files/dissertation-Jian.pdf Session Aware Recommender System in E-Commerce] - [[Jian Wang]]
* [http://doras.dcu.ie/17737/ Social contextuality and conversational recommender systems] - [[Eoin Hurrell]]
* [http://liris.cnrs.fr/Documents/Liris-6406.pdf Trace-Based Reasoning for User Assistance and Recommendations] - [[Raafat Zarka]]
* [http://kth.diva-portal.org/smash/record.jsf?pid=diva2:606503 Trust-Based User Profiling] - [[Nima Dokoohaki]]
* [http://scidok.sulb.uni-saarland.de/volltexte/2013/5528/pdf/Boehmer_2013_MobileApplicationUsage_low.pdf Understanding and supporting mobile application usage] - [[Matthias Böhmer]]
* [http://d-scholarship.pitt.edu/19733/ User Contrallability in a Hybrid Recommender System] - [[Denis Parra]]

=== 2012 ===
* [http://library.epfl.ch/en/theses/?nr=5318 Design and User Perception Issues for Personality-Engaged Recommender Systems] - [[Rong Hu]]
* [http://www.di.uniba.it/~swap/papers/musto_thesis.pdf Enhanced Vector Space Models for Content-based Recommender Systems] - [[Cataldo Musto]]
* [http://dalspace.library.dal.ca/bitstream/handle/10222/14735/Lipczak,%20Marek,%20PhD,%20CS,%20March%202012.pdf?sequence=5 Hybrid Tag Recommendation in Collaborative Tagging Systems] - [[Marek Lipczak]]
* [http://gradworks.umi.com/35/05/3505275.html Interaction Methods for Large Scale Graph Visualization Systems --- Using Manipulation to Aid Discovery] - [[Brynjar Gretarsson]]
* [http://www.evazangerle.at/wp-content/papercite-data/pdf/evaphd.pdf Leveraging Recommender Systems for the Creation and Maintenance of Structure within Collaborative Social Media Platforms] - [[Eva Zangerle]]
* [https://eldorado.tu-dortmund.de/bitstream/2003/29661/1/Dissertation.pdf Leveraging Tagging Data for Recommender Systems] - [[Fatih Gedikli]]
* [http://cseweb.ucsd.edu/~bmcfee/papers/bmcfee_dissertation.pdf More like this: machine learning approaches to music similarity] - [[Brian McFee]]
* [http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-17249 On some Challenges for Online Trust and Reputation Systems] - [[Mozhgan Tavakolifard]]
* [http://www.l3s.de/~krestel/cmsimple3_2/?Publications:Others:PhD_Thesis_12 On the Use of Language Models and Topic Models in the Web: New Algorithms for Filtering, Classification, Ranking, and Recommendation] - [[Ralf Krestel]]
* [http://ir.ii.uam.es/~alejandro/thesis/thesis-bellogin.pdf Performance prediction and evaluation in Recommender Systems: an Information Retrieval perspective] - [[Alejandro Bellogin]]
* [http://eprints.qut.edu.au/59507/1/Noraswaliza_Abdullah_Thesis.pdf Integrating collaborative filtering and matching-based search for product recommendation] - [[Noraswaliza Abdullah]]
* [http://www.uns.ac.rs/sr/doktorske/aleksandraKlasnjaMilicevic/disertacija.pdf Personalized Recommendation Based on Collaborative Tagging Techniques for an E-learning System] - [[Aleksandra Klašnja‐Milićević]]
* [http://arxiv.org/pdf/1203.4487v2 Recommender systems in industrial contexts] - [[Frank Meyer]]
* [http://opus.bsz-bw.de/ubhi/volltexte/2012/167/pdf/item_recommendation.pdf Supervised Machine Learning Methods for Item Recommendation] - [[Zeno Gantner]]
* [http://gradworks.umi.com/35/23/3523081.html Understanding Consistency of Recommender Systems: Behavioral and Algorithmic Perspectives] - [[Jingjing Zhang]]

=== 2011 ===
* [http://users.cecs.anu.edu.au/~sguo/thesis.pdf Bayesian Recommender Systems: Models and Algorithms] - [[Shengbo Guo]]
* [http://www.inf.unibz.it/~lbaltrunas/doc/linas_phd_thesis.pdf Context-Aware Collaborative Filtering Recommender Systems] - [[Linas Baltrunas]]
* [http://benfields.net/bfields_thesis.pdf Contextualize Your Listening: The Playlist as Recommendation Engine] - [[Ben Fields]]
* [http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED534217&ERICExtSearch_SearchType_0=no&accno=ED534217 Deployment of Recommender Systems: Operational and Strategic Issues] - [[Abhijeet Ghoshal]]
* [http://espace.library.uq.edu.au/view/UQ:263213 Effective and Efficient Collaborative Filtering] - [[Yi Ding]]
* [http://www.cse.cuhk.edu.hk/lyu/_media/students/thesisxin9.pdf Effective Fusion-based Approaches for Recommender Systems] - [[Xin Xin]]
* [http://www.aka-verlag.com/de/detail?ean=978-3-89838-332-5 Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems] - [[Robert Jäschke]]
* [http://uclab.khu.ac.kr/resources/thesis/PhD_Thesis_Weiwei.pdf Improved Trust-Aware Recommender System using Small-Worldness of Trust Networks] - [[Weiwei Yuan]]
* [http://conservancy.umn.edu/handle/117321 Personalized Recommendation in Social Network Sites] - [[Jilin Chen]]
* [http://lac-repo-live7.is.ed.ac.uk/bitstream/1842/5770/1/Givon2011.pdf Predicting and using social tags to improve the accuracy and transparency of recommender systems] - [[Sharon Givon]]
* [http://slavnik.fe.uni-lj.si/markot/uploads/Main/2010_tkalcic_phd.pdf Recognition and usage of emotive parameters in recommender systems] - [[Marko Tkalčič]]
* [http://gradworks.umi.com/34/79/3479962.html Swarm intellilgence for clustering dynamic data sets for web usage mining and personalization] - [[Esin Saka]]
* [http://eprints.qut.edu.au/41879/1/Huizhi_Liang_Thesis.pdf User profiling based on folksonomy information in Web 2.0 for personalized recommender systems] - [[Huizhi Liang]]
* [http://eprints.qut.edu.au/49168/1/Touhid_Bhuiyan_Thesis.pdf Trust-based automated recommendation making] - [[Touhid Bhuiyan]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/1687272 Using Data Mining for Facilitating User Contributions in the Social Semantic Web] - [[Maryam Ramezani]]
* [http://gradworks.umi.com/34/49/3449549.html Visualization of music relational information sources for analysis, navigation, and discovery] - [[Justin Donaldson]]

=== 2010 ===
* [http://www.amazon.com/Domain-Independent-Framework-Intelligent-Recommendations-Application/dp/3838113756 A Domain-Independent Framework for Intelligent Recommendations] - [[Jörn David]]
* [http://www.cp.jku.at/people/seyerlehner/supervised/seyerlehner_phd.pdf Content-Based Music Recommender Systems: Beyond simple Frame-Level Audio Similarity] - [[Klaus Seyerlehner]]
* [http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-16897-0 Context-Aware Ranking with Factorization Models] - [[Steffen Rendle]]
* [http://www.cs.ucl.ac.uk/staff/n.lathia/thesis.html Evaluating Collaborative Filtering Over Time] - [[Neal Lathia]]
* [http://opus.kobv.de/tuberlin/volltexte/2010/2695/pdf/wetzker_robert.pdf Graph-Based Recommendation in Broad Folksonomies] - [[Robert Wetzker]]
* [http://lib.tkk.fi/Diss/2010/isbn9789526031514/isbn9789526031514.pdf Methods and Applications for Ontology-based Recommender Systems] - [[Tuukka Ruotsalo]]
* [http://theses.gla.ac.uk/2132/01/2010hopfgartner1phd.pdf Personalised video retrieval: application of implicit feedback and semantic user profiles] - [[Frank Hopfgartner]]
* [http://www.dsc.ufcg.edu.br/~lbmarinho/homepage/pub/thesis_marinho.pdf Recommender Systems for Social Tagging Systems] - [[Leandro Balby Marinho]]
* [http://www.verlagdrkovac.de/3-8300-5081-X.htm Recommender Systeme für produktbegleitende Dienstleistungen] - [[Margarethe Frohs]]
* [http://wrap.warwick.ac.uk/3759/1/WRAP_THESIS_Li_2010.pdf Relational clustering models for knowledge discovery and recommender systems] - [[Tao Li]]
* [https://biblio.ugent.be/publication/986279/file/1886805.pdf Trust networks for recommender systems] - [[Patricia Victor]]
* [http://gradworks.umi.com/34/70/3470158.html User session and history modeling for collaborative visualization] - [[Fanhai Yang]]

=== 2009 ===
* [http://opus.kobv.de/tuberlin/volltexte/2009/2245/pdf/lommatzsch_andreas.pdf Eine offene Architektur für die agentenbasierte, adaptive, personalisierte Informationsfilterung] (German) - [[Andreas Lommatzsch]]
* [http://www.abdn.ac.uk/~csc284/Nava%20Tintarev_PhD_Thesis_%282010%29.pdf Explaining recommendations] - [[Nava Tintarev]]
* [http://www.omikk.bme.hu/collections/phd/Villamosmernoki_es_Informatikai_Kar/2010/Pilaszy_Istvan/ertekezes.pdf Factorization-Based Large Scale Recommendation Algorithms] - [[István Pilászy]]
* [http://www.shilad.com/shilads_thesis.pdf Nurturing Tagging Communities] - [[Shilad Sen]]
* [http://repository.upenn.edu/dissertations/AAI3363295/ Recommender systems and market diversity] - [[Daniel M. Fleder]]
* [http://itlab.dbit.dk/~toine/?page_id=6 Recommender Systems for Social Bookmarking] - [[Toine Bogers]]
* [http://svn.egovmon.no/svn/phdgoodwin/thesis/referenceexample/MMR_thesis_afterDefense.pdf Towards Efficient Music Similarity Search, Ranking, and Recommendation] - [[Maria Magdalena Ruxanda]]

=== 2008 ===
* [http://essay.utwente.nl/59711/1/MA_thesis_J_de_Wit.pdf Evaluating recommender systems : an evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context] - [[Joost de Wit]]
* [http://arantxa.ii.uam.es/~cantador/doc/2008/thesis08.zip Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach] - [[Iván Cantador]]
* [http://eprints.qut.edu.au/29165/2/Li-Tung_Weng_Thesis.pdf Information Enrichment for Quality Recommender Systems] - [[Li-Tung Weng]]
* [http://tomheath.com/thesis/html Information-seeking on the Web with Trusted Social Networks – from Theory to Systems] - [[Tom Heath]]
* [http://www.cs.umass.edu/~marlin/research/phd_thesis/marlin-phd-thesis.pdf Missing Data Problems in Machine Learning] - [[Benjamin Marlin]]
* [http://mtg.upf.edu/node/1217 Music Recommendation and Discovery in the Long Tail] - [[Òscar Celma]]
* [http://hal.inria.fr/docs/00/34/83/70/PDF/TeseFinal.pdf Recommender System based on Personality Traits] - [[Maria Augusta Silveira Netto Nunes]]
* [http://winnie.kuis.kyoto-u.ac.jp/members/yoshii/d-thesis-yoshii.pdf Studies on Hybrid Music Recommendation Using Timbral and Rhythmic Features] - [[Kazuyoshi Yoshii]]
* [http://www.peterlang.com/index.cfm?event=cmp.ccc.seitenstruktur.detailseiten&seitentyp=produkt&pk=56341&cid=367 Towards Metadata-aware Algorithms for Recommender Systems] - [[Karen H. L. Tso-Sutter]]
* [http://hci.epfl.ch/members/lichen/EPFL_TH4140.pdf User Decision Improvement and Trust Building in Product Recommender Systems] - [[Li Chen]]

=== 2007 ===
* [http://www.ulrichpaquet.com/Papers/PhDThesis.pdf Bayesian Inference for Latent Variable Models] - [[Ulrich Paquet]]
* [http://www-users.cs.umn.edu/~arashid/thesis.pdf Mining Influence in Recommender Systems] - [[Al Mamunur Rashid]]
* [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]

=== 2006 ===
* [http://dl.acm.org/citation.cfm?id=1269509 Designing social interactions with animated avatars and speech output for product recommendation agents in electronic commerce] - [[Lingyun Qiu]]
* [http://www-users.cs.umn.edu/~mcnee/mcnee-thesis-preprint.pdf Meeting User Information Needs in Recommender Systems] - [[Sean McNee]]

=== 2005 ===
* [http://eprints.ecs.soton.ac.uk/10692/1/wei-phd-thesis.pdf A Market-Based Approach to Recommender Systems] - [[Yan Zheng Wei]]
* [http://dl.acm.org/citation.cfm?id=1123874 Explanet: a learning tool and hybrid recommender system for student-authored explanations] - [[Jessica Masters]]
* Hybrid recommendation techniques based on user profiles - [[Pasquale Lops]]
* [http://www.princeton.edu/~smorris/pdfs/PhD/Ozmen.pdf Information Transmission and Recommender Systems] - [[Deran Özmen]]
* [https://doc.telin.nl/dsweb/Get/Document-56873 Supporting People In Finding Information: Hybrid Recommender Systems and Goal-Based Structuring] - [[Mark van Setten]]
* [http://www.freidok.uni-freiburg.de/volltexte/1804/pdf/Thesis.pdf Towards Decentralized Recommender Systems] - [[Cai-Nicolas Ziegler]]
* [http://dspace.mit.edu/bitstream/handle/1721.1/31137/61184336.pdf?sequence=1 Use of Discrete Choice Models with Recommender Systems] - [[Bassam H. Chaptini]]

=== 2004 ===

=== 2003 ===
* [http://eia.udg.es/~mmontane/montaner-thesis03.pdf Collaborative recommender agents based on case-based reasoning and trust] - [[Miquel Montaner]]
* [https://circle.ubc.ca/bitstream/handle/2429/14933/ubc_2003-859525.pdf?sequence=1 The Impact of Internalization and Familiarity on Trust and Adoption of Recommendation Agents] - [[Sherrie Komiak]]
* [http://knuth.luther.edu/~bmiller/Papers/thesis.pdf Toward a Personal Recommender System] - [[Bradley N. Miller]]

=== 2002 ===
* [http://dl.acm.org/citation.cfm?id=935978 Recommendation as classification and recommendation as matching: two information-centered approaches to recommendation] - [[Chumki Basu]]

=== 2001 ===
* [http://www.patrickbaudisch.com/publications/2001-Baudisch-Dissertation-DynamicInformationFiltering.pdf Dynamic Information Filtering] - [[Patrick Baudisch]]
* [http://www.cs.uni.edu/~schafer/publications/schafer_thesis.pdf MetaLens: A Framework for Multsource Recommendations] - [[Ben Schafer]]
* [http://www-users.cs.umn.edu/~sarwar/thesis.ps Sparsity, scalability, and distribution in recommender systems] - [[Badrul Munir Sarwar]]

=== 2000 ===

=== 1999 ===

=== 1998 ===

=== 1997 ===
* Recommender Systems for Problem Solving Environments - [[Naren Ramakrishnan]]

== External links ==
* [http://pampalk.at/mir-phds/ PhD Theses and Doctoral Dissertations Related to Music Information Retrieval]
* [http://www.mendeley.com/groups/3058301/recommender-system-dissertations/papers/ Mendeley collection of Recommender System dissertations] (based on this page)

[[Category: List|Dissertation]]

RankSys

2015-03-27T13:51:12Z

Saulvargas:

RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ir-uam.github.io/RankSys/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

To date, the publicly available version of this framework includes the modules that implement novelty and diversity metrics and re-ranking techniques and the required core components of the framework:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-diversity, which contains the novelty and diversity metrics and re-ranking strategies.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://github.com/ir-uam/RankSys/wiki wiki of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/ir-uam/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ir-uam.github.io/RankSys/
* GitHub repository: https://github.com/ir-uam/RankSys
* Wiki: http://github.com/ir-uam/RankSys/wiki

[[Category: Java]]
[[Category: Software]]

RankSys

2015-03-27T13:50:39Z

Saulvargas:

RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ir-uam.github.io/RankSys/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

To date, the publicly available version of this framework includes the modules that implement novelty and diversity metrics and re-ranking techniques and the required core components of the framework:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-diversity, which contains the novelty and diversity metrics and re-ranking strategies.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://ir-uam.github.io/RankSys/ wiki of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/ir-uam/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ir-uam.github.io/RankSys/
* GitHub repository: https://github.com/ir-uam/RankSys
* Wiki: http://github.com/ir-uam/RankSys/wiki

[[Category: Java]]
[[Category: Software]]

List of recommender system dissertations

2015-03-27T11:15:25Z

Saulvargas:

[[Recommender systems]] related dissertations by year.

Dissertation within a year are sorted alphabetically by title.

=== 2015 ===
* [http://ir.ii.uam.es/saul/saulvargas-thesis.pdf Novelty and Diversity Evaluation and Enhancement in Recommender Systems] - [[Saúl Vargas]]

=== 2014 ===
* [http://scholarlyrepository.miami.edu/cgi/viewcontent.cgi?article=2209&context=oa_dissertations A Model-Based Music Recommendation System for Individual Users and Implicit User Groups] - [[Yajie Hu]]
* [http://infoscience.epfl.ch/record/199806 Aggregating Information from the Crowd: ratings, recommendations and predictions] - [[Florent Garcin]]
* [http://wanlab.poly.edu/xiwang/doc/thesis.pdf Collaborative Filtering Based Social Recommender Systems] - [[Xiwang Yang]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/3158723 Cross-domain Recommendations based on semantically-enhanced User Web Behavior] - [[Julia Hoxha]]
* [http://www.win.tue.nl/ipa/?event=cryptographically-enhanced-privacy-for-recommender-systems Cryptographically-Enhanced Privacy for Recommender Systems] - [[Arjan Jeckmans]]
* [http://conservancy.umn.edu/handle/11299/167084 Database management system support for collaborative filtering recommender systems] - [[Mohamed Sarwat]]
* [http://bit.ly/simonphd Dynamic Generation of Personalized Hybrid Recommender Systems] - [[Simon Dooms]]
* Enhancing Discovery in Geoportals: Geo-Enrichment, Semantic Enhancement and Recommendation Strategies for Geo-Information Discovery - [[Bernhard Vockner]]
* [https://dl.dropboxusercontent.com/u/13894587/hcorona-msc-2014.pdf Enhancing Content Discovery. An Evaluation and Classification Perspective] - [[Humberto Corona]]
* [http://www.lsi.upc.edu/~vcodina/phd.pdf Exploiting Distributional Semantics for Content-Based and Context-Aware Recommendation] - [[Victor Codina]]
* [http://micheletrevisiol.com/data/Exploiting_Implicit_User_Activity_for_Media_Recommendation.pdf Exploiting Implicit User Activity for Media Recommendation] - [[Michele Trevisiol]]
* [http://arks.princeton.edu/ark:/88435/dsp01nv935299q Information Aggregation in Quantized Consensus, Recommender Systems, and Ranking] - [[Shang Shang]]
* [http://repository.lib.ncsu.edu/ir/bitstream/1840.16/9727/1/etd.pdf More Usable Recommendation Systems for Improving Software Quality] - [[Yoonki Song]]
* [http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=2550&context=etd Next Generation of Recommender Systems: Algorithms and Applications] - [[Lei Li]]
* [http://ethesis.unifr.ch/theses/downloads.php?file=TeranL.pdf SmartParticipation: A Fuzzy-Based Recommender System for Political Community-Building] - [[Luis Fernando Terán Tamayo]]
* [http://elehack.net/research/thesis/ Towards Recommender Engineering: Tools and Experiments for Identifying Recommender Differences] - [[Michael Ekstrand]]
* [http://tampub.uta.fi/bitstream/handle/10024/95965/978-951-44-9551-9.pdf User Factors in Recommender Systems: Case Studies in e-Commerce, News Recommending, and e-Learning] - [[Juha Leino]]

=== 2013 ===
* [http://interactivesystems.info/publications/a-conceptual-model-and-a-software-framework-for-developing-context-aware-hybrid-recommender-systems A conceptual model and a software framework for developing context aware hybrid recommender systems] - [[Tim Hussein]]
* [http://shodhganga.inflibnet.ac.in/handle/10603/24399 Effective tag recommendation system based on topic ontology] - [[V Subramaniyaswamy]]
* [http://eprints.ucm.es/24533/ Estrategias de recomendación basadas en conocimiento para la localización personalizada de recursos en repositorios educativos] (Spanish) - [[Almudena Ruiz-Iniesta]]
* [http://opus4.kobv.de/opus4-tuberlin/frontdoor/index/index/docId/3681 Evaluating the Accuracy and Utility of Recommender Systems] - [[Alan Said]]
* [https://biblio.ugent.be/input/download?func=downloadFile&recordOId=4163727&fileOId=4163752 Improved online services by personalized recommendations and optimal quality of experience parameters] - [[Toon De Pessemier]]
* [http://hdl.handle.net/10059/859 Integrating Content and Semantic Representations for Music Recommendation] - [[Ben Horsburgh]]
* [http://escholarship.org/uc/item/4xw874p5#page-1 Latent feature models for dyadic prediction] - [[Aditya Krishna Menon]]
* [http://repository.tudelft.nl/assets/uuid:f7d3977e-f191-40d4-8f27-784a32902a55/thesis_yueshi.pdf Ranking and Context-awareness in Recommender Systems] - [[Yue Shi]]
* [http://digitool-uam.greendata.es//exlibris/dtl/d3_1/apache_media/L2V4bGlicmlzL2R0bC9kM18xL2FwYWNoZV9tZWRpYS82NjA5NQ==.pdf Recommender Systems and Time Context: Characterization of a Robust Evaluation Protocol to Increase Reliability of Measured Improvemenets] - [[Pedro Campos]]
* [http://users.soe.ucsc.edu/~jwang30/index.files/dissertation-Jian.pdf Session Aware Recommender System in E-Commerce] - [[Jian Wang]]
* [http://doras.dcu.ie/17737/ Social contextuality and conversational recommender systems] - [[Eoin Hurrell]]
* [http://liris.cnrs.fr/Documents/Liris-6406.pdf Trace-Based Reasoning for User Assistance and Recommendations] - [[Raafat Zarka]]
* [http://kth.diva-portal.org/smash/record.jsf?pid=diva2:606503 Trust-Based User Profiling] - [[Nima Dokoohaki]]
* [http://scidok.sulb.uni-saarland.de/volltexte/2013/5528/pdf/Boehmer_2013_MobileApplicationUsage_low.pdf Understanding and supporting mobile application usage] - [[Matthias Böhmer]]
* [http://d-scholarship.pitt.edu/19733/ User Contrallability in a Hybrid Recommender System] - [[Denis Parra]]

=== 2012 ===
* [http://library.epfl.ch/en/theses/?nr=5318 Design and User Perception Issues for Personality-Engaged Recommender Systems] - [[Rong Hu]]
* [http://www.di.uniba.it/~swap/papers/musto_thesis.pdf Enhanced Vector Space Models for Content-based Recommender Systems] - [[Cataldo Musto]]
* [http://dalspace.library.dal.ca/bitstream/handle/10222/14735/Lipczak,%20Marek,%20PhD,%20CS,%20March%202012.pdf?sequence=5 Hybrid Tag Recommendation in Collaborative Tagging Systems] - [[Marek Lipczak]]
* [http://gradworks.umi.com/35/05/3505275.html Interaction Methods for Large Scale Graph Visualization Systems --- Using Manipulation to Aid Discovery] - [[Brynjar Gretarsson]]
* [http://www.evazangerle.at/wp-content/papercite-data/pdf/evaphd.pdf Leveraging Recommender Systems for the Creation and Maintenance of Structure within Collaborative Social Media Platforms] - [[Eva Zangerle]]
* [https://eldorado.tu-dortmund.de/bitstream/2003/29661/1/Dissertation.pdf Leveraging Tagging Data for Recommender Systems] - [[Fatih Gedikli]]
* [http://cseweb.ucsd.edu/~bmcfee/papers/bmcfee_dissertation.pdf More like this: machine learning approaches to music similarity] - [[Brian McFee]]
* [http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-17249 On some Challenges for Online Trust and Reputation Systems] - [[Mozhgan Tavakolifard]]
* [http://www.l3s.de/~krestel/cmsimple3_2/?Publications:Others:PhD_Thesis_12 On the Use of Language Models and Topic Models in the Web: New Algorithms for Filtering, Classification, Ranking, and Recommendation] - [[Ralf Krestel]]
* [http://ir.ii.uam.es/~alejandro/thesis/thesis-bellogin.pdf Performance prediction and evaluation in Recommender Systems: an Information Retrieval perspective] - [[Alejandro Bellogin]]
* [http://eprints.qut.edu.au/59507/1/Noraswaliza_Abdullah_Thesis.pdf Integrating collaborative filtering and matching-based search for product recommendation] - [[Noraswaliza Abdullah]]
* [http://www.uns.ac.rs/sr/doktorske/aleksandraKlasnjaMilicevic/disertacija.pdf Personalized Recommendation Based on Collaborative Tagging Techniques for an E-learning System] - [[Aleksandra Klašnja‐Milićević]]
* [http://arxiv.org/pdf/1203.4487v2 Recommender systems in industrial contexts] - [[Frank Meyer]]
* [http://opus.bsz-bw.de/ubhi/volltexte/2012/167/pdf/item_recommendation.pdf Supervised Machine Learning Methods for Item Recommendation] - [[Zeno Gantner]]
* [http://gradworks.umi.com/35/23/3523081.html Understanding Consistency of Recommender Systems: Behavioral and Algorithmic Perspectives] - [[Jingjing Zhang]]

=== 2011 ===
* [http://users.cecs.anu.edu.au/~sguo/thesis.pdf Bayesian Recommender Systems: Models and Algorithms] - [[Shengbo Guo]]
* [http://www.inf.unibz.it/~lbaltrunas/doc/linas_phd_thesis.pdf Context-Aware Collaborative Filtering Recommender Systems] - [[Linas Baltrunas]]
* [http://benfields.net/bfields_thesis.pdf Contextualize Your Listening: The Playlist as Recommendation Engine] - [[Ben Fields]]
* [http://www.eric.ed.gov/ERICWebPortal/search/detailmini.jsp?_nfpb=true&_&ERICExtSearch_SearchValue_0=ED534217&ERICExtSearch_SearchType_0=no&accno=ED534217 Deployment of Recommender Systems: Operational and Strategic Issues] - [[Abhijeet Ghoshal]]
* [http://espace.library.uq.edu.au/view/UQ:263213 Effective and Efficient Collaborative Filtering] - [[Yi Ding]]
* [http://www.cse.cuhk.edu.hk/lyu/_media/students/thesisxin9.pdf Effective Fusion-based Approaches for Recommender Systems] - [[Xin Xin]]
* [http://www.aka-verlag.com/de/detail?ean=978-3-89838-332-5 Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems] - [[Robert Jäschke]]
* [http://uclab.khu.ac.kr/resources/thesis/PhD_Thesis_Weiwei.pdf Improved Trust-Aware Recommender System using Small-Worldness of Trust Networks] - [[Weiwei Yuan]]
* [http://conservancy.umn.edu/handle/117321 Personalized Recommendation in Social Network Sites] - [[Jilin Chen]]
* [http://lac-repo-live7.is.ed.ac.uk/bitstream/1842/5770/1/Givon2011.pdf Predicting and using social tags to improve the accuracy and transparency of recommender systems] - [[Sharon Givon]]
* [http://slavnik.fe.uni-lj.si/markot/uploads/Main/2010_tkalcic_phd.pdf Recognition and usage of emotive parameters in recommender systems] - [[Marko Tkalčič]]
* [http://gradworks.umi.com/34/79/3479962.html Swarm intellilgence for clustering dynamic data sets for web usage mining and personalization] - [[Esin Saka]]
* [http://eprints.qut.edu.au/41879/1/Huizhi_Liang_Thesis.pdf User profiling based on folksonomy information in Web 2.0 for personalized recommender systems] - [[Huizhi Liang]]
* [http://eprints.qut.edu.au/49168/1/Touhid_Bhuiyan_Thesis.pdf Trust-based automated recommendation making] - [[Touhid Bhuiyan]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/1687272 Using Data Mining for Facilitating User Contributions in the Social Semantic Web] - [[Maryam Ramezani]]
* [http://gradworks.umi.com/34/49/3449549.html Visualization of music relational information sources for analysis, navigation, and discovery] - [[Justin Donaldson]]

=== 2010 ===
* [http://www.amazon.com/Domain-Independent-Framework-Intelligent-Recommendations-Application/dp/3838113756 A Domain-Independent Framework for Intelligent Recommendations] - [[Jörn David]]
* [http://www.cp.jku.at/people/seyerlehner/supervised/seyerlehner_phd.pdf Content-Based Music Recommender Systems: Beyond simple Frame-Level Audio Similarity] - [[Klaus Seyerlehner]]
* [http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-16897-0 Context-Aware Ranking with Factorization Models] - [[Steffen Rendle]]
* [http://www.cs.ucl.ac.uk/staff/n.lathia/thesis.html Evaluating Collaborative Filtering Over Time] - [[Neal Lathia]]
* [http://opus.kobv.de/tuberlin/volltexte/2010/2695/pdf/wetzker_robert.pdf Graph-Based Recommendation in Broad Folksonomies] - [[Robert Wetzker]]
* [http://lib.tkk.fi/Diss/2010/isbn9789526031514/isbn9789526031514.pdf Methods and Applications for Ontology-based Recommender Systems] - [[Tuukka Ruotsalo]]
* [http://theses.gla.ac.uk/2132/01/2010hopfgartner1phd.pdf Personalised video retrieval: application of implicit feedback and semantic user profiles] - [[Frank Hopfgartner]]
* [http://www.dsc.ufcg.edu.br/~lbmarinho/homepage/pub/thesis_marinho.pdf Recommender Systems for Social Tagging Systems] - [[Leandro Balby Marinho]]
* [http://www.verlagdrkovac.de/3-8300-5081-X.htm Recommender Systeme für produktbegleitende Dienstleistungen] - [[Margarethe Frohs]]
* [http://wrap.warwick.ac.uk/3759/1/WRAP_THESIS_Li_2010.pdf Relational clustering models for knowledge discovery and recommender systems] - [[Tao Li]]
* [https://biblio.ugent.be/publication/986279/file/1886805.pdf Trust networks for recommender systems] - [[Patricia Victor]]
* [http://gradworks.umi.com/34/70/3470158.html User session and history modeling for collaborative visualization] - [[Fanhai Yang]]

=== 2009 ===
* [http://opus.kobv.de/tuberlin/volltexte/2009/2245/pdf/lommatzsch_andreas.pdf Eine offene Architektur für die agentenbasierte, adaptive, personalisierte Informationsfilterung] (German) - [[Andreas Lommatzsch]]
* [http://www.abdn.ac.uk/~csc284/Nava%20Tintarev_PhD_Thesis_%282010%29.pdf Explaining recommendations] - [[Nava Tintarev]]
* [http://www.omikk.bme.hu/collections/phd/Villamosmernoki_es_Informatikai_Kar/2010/Pilaszy_Istvan/ertekezes.pdf Factorization-Based Large Scale Recommendation Algorithms] - [[István Pilászy]]
* [http://www.shilad.com/shilads_thesis.pdf Nurturing Tagging Communities] - [[Shilad Sen]]
* [http://repository.upenn.edu/dissertations/AAI3363295/ Recommender systems and market diversity] - [[Daniel M. Fleder]]
* [http://itlab.dbit.dk/~toine/?page_id=6 Recommender Systems for Social Bookmarking] - [[Toine Bogers]]
* [http://svn.egovmon.no/svn/phdgoodwin/thesis/referenceexample/MMR_thesis_afterDefense.pdf Towards Efficient Music Similarity Search, Ranking, and Recommendation] - [[Maria Magdalena Ruxanda]]

=== 2008 ===
* [http://essay.utwente.nl/59711/1/MA_thesis_J_de_Wit.pdf Evaluating recommender systems : an evaluation framework to predict user satisfaction for recommender systems in an electronic programme guide context] - [[Joost de Wit]]
* [http://arantxa.ii.uam.es/~cantador/doc/2008/thesis08.zip Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach] - [[Iván Cantador]]
* [http://eprints.qut.edu.au/29165/2/Li-Tung_Weng_Thesis.pdf Information Enrichment for Quality Recommender Systems] - [[Li-Tung Weng]]
* [http://tomheath.com/thesis/html Information-seeking on the Web with Trusted Social Networks – from Theory to Systems] - [[Tom Heath]]
* [http://www.cs.umass.edu/~marlin/research/phd_thesis/marlin-phd-thesis.pdf Missing Data Problems in Machine Learning] - [[Benjamin Marlin]]
* [http://mtg.upf.edu/node/1217 Music Recommendation and Discovery in the Long Tail] - [[Òscar Celma]]
* [http://hal.inria.fr/docs/00/34/83/70/PDF/TeseFinal.pdf Recommender System based on Personality Traits] - [[Maria Augusta Silveira Netto Nunes]]
* [http://winnie.kuis.kyoto-u.ac.jp/members/yoshii/d-thesis-yoshii.pdf Studies on Hybrid Music Recommendation Using Timbral and Rhythmic Features] - [[Kazuyoshi Yoshii]]
* [http://www.peterlang.com/index.cfm?event=cmp.ccc.seitenstruktur.detailseiten&seitentyp=produkt&pk=56341&cid=367 Towards Metadata-aware Algorithms for Recommender Systems] - [[Karen H. L. Tso-Sutter]]
* [http://hci.epfl.ch/members/lichen/EPFL_TH4140.pdf User Decision Improvement and Trust Building in Product Recommender Systems] - [[Li Chen]]

=== 2007 ===
* [http://www.ulrichpaquet.com/Papers/PhDThesis.pdf Bayesian Inference for Latent Variable Models] - [[Ulrich Paquet]]
* [http://www-users.cs.umn.edu/~arashid/thesis.pdf Mining Influence in Recommender Systems] - [[Al Mamunur Rashid]]
* [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]

=== 2006 ===
* [http://dl.acm.org/citation.cfm?id=1269509 Designing social interactions with animated avatars and speech output for product recommendation agents in electronic commerce] - [[Lingyun Qiu]]
* [http://www-users.cs.umn.edu/~mcnee/mcnee-thesis-preprint.pdf Meeting User Information Needs in Recommender Systems] - [[Sean McNee]]

=== 2005 ===
* [http://eprints.ecs.soton.ac.uk/10692/1/wei-phd-thesis.pdf A Market-Based Approach to Recommender Systems] - [[Yan Zheng Wei]]
* [http://dl.acm.org/citation.cfm?id=1123874 Explanet: a learning tool and hybrid recommender system for student-authored explanations] - [[Jessica Masters]]
* Hybrid recommendation techniques based on user profiles - [[Pasquale Lops]]
* [http://www.princeton.edu/~smorris/pdfs/PhD/Ozmen.pdf Information Transmission and Recommender Systems] - [[Deran Özmen]]
* [https://doc.telin.nl/dsweb/Get/Document-56873 Supporting People In Finding Information: Hybrid Recommender Systems and Goal-Based Structuring] - [[Mark van Setten]]
* [http://www.freidok.uni-freiburg.de/volltexte/1804/pdf/Thesis.pdf Towards Decentralized Recommender Systems] - [[Cai-Nicolas Ziegler]]
* [http://dspace.mit.edu/bitstream/handle/1721.1/31137/61184336.pdf?sequence=1 Use of Discrete Choice Models with Recommender Systems] - [[Bassam H. Chaptini]]

=== 2004 ===

=== 2003 ===
* [http://eia.udg.es/~mmontane/montaner-thesis03.pdf Collaborative recommender agents based on case-based reasoning and trust] - [[Miquel Montaner]]
* [https://circle.ubc.ca/bitstream/handle/2429/14933/ubc_2003-859525.pdf?sequence=1 The Impact of Internalization and Familiarity on Trust and Adoption of Recommendation Agents] - [[Sherrie Komiak]]
* [http://knuth.luther.edu/~bmiller/Papers/thesis.pdf Toward a Personal Recommender System] - [[Bradley N. Miller]]

=== 2002 ===
* [http://dl.acm.org/citation.cfm?id=935978 Recommendation as classification and recommendation as matching: two information-centered approaches to recommendation] - [[Chumki Basu]]

=== 2001 ===
* [http://www.patrickbaudisch.com/publications/2001-Baudisch-Dissertation-DynamicInformationFiltering.pdf Dynamic Information Filtering] - [[Patrick Baudisch]]
* [http://www.cs.uni.edu/~schafer/publications/schafer_thesis.pdf MetaLens: A Framework for Multsource Recommendations] - [[Ben Schafer]]
* [http://www-users.cs.umn.edu/~sarwar/thesis.ps Sparsity, scalability, and distribution in recommender systems] - [[Badrul Munir Sarwar]]

=== 2000 ===

=== 1999 ===

=== 1998 ===

=== 1997 ===
* Recommender Systems for Problem Solving Environments - [[Naren Ramakrishnan]]

== External links ==
* [http://pampalk.at/mir-phds/ PhD Theses and Doctoral Dissertations Related to Music Information Retrieval]
* [http://www.mendeley.com/groups/3058301/recommender-system-dissertations/papers/ Mendeley collection of Recommender System dissertations] (based on this page)

[[Category: List|Dissertation]]

RankSys

2015-03-24T15:23:08Z

Saulvargas: Created page with "RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently d..."

RankSys is a new framework for the implementation and evaluation of recommendation algorithms and techniques that has resulted from a line of research work that is currently documented in several publications and a PhD thesis (see [http://ir-uam.github.io/RankSys/ RankSys webpage]). While it is envisioned as a framework for the generic experimentation of recommendation technologies, it includes substantial support focusing on the evaluation and enhancement of novelty and diversity. RankSys derives its name from explicitly targeting the ranking task problem, rather than rating prediction. This decision is reflected in the design of the different core interfaces and components of the framework.

The framework has been programmed with Java 8. We take advantage of many of the new features of the language, such as the use of lambda functions, Stream's and facilities for automatic parallelization of the code. The code licensed under the GPL V3, which allows the free use, study, distribution and modification of the software as long as derived works are distributed under the same license.

To date, the publicly available version of this framework includes the modules that implement novelty and diversity metrics and re-ranking techniques and the required core components of the framework:

* RankSys-core, which contains the common and auxiliary classes of the framework.
* RankSys-metrics, which contains the interfaces and common components for defining metrics.
* RankSys-diversity, which contains the novelty and diversity metrics and re-ranking strategies.
* RankSys-examples, which provides examples of usage of the previous modules.

If you want to know more, the [http://ir-uam.github.io/RankSys/ webpage of the project] provides a high-level description of the different components of the current release of the software and [https://github.com/ir-uam/RankSys GitHub] hosts the source code.

== External links ==
* Webpage: http://ir-uam.github.io/RankSys/
* GitHub repository: https://github.com/ir-uam/RankSys

[[Category: Java]]
[[Category: Software]]