RecSysWiki - User contributions [en]

List of recommender system dissertations

2015-01-03T16:27:17Z

Sidooms: /* 2014 */

[[Recommender systems]] related dissertations by year.

Dissertation within a year are sorted alphabetically by title.

=== 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://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]]

List of RecSys-relevant Conferences

2013-10-25T13:02:33Z

Sidooms: /* Journals */

Looking for a place to publish your recommender system work?

'''Interests:'''

Web Personalisation, Recommender/Reputation Systems, User Modeling, Decision Support Systems, Artificial Intelligence, Data Mining, Data Clustering, Data Management, Scalability, Web 2.0, Social Network Analysis

'''Note:'''

The external links to these conferences often change (annually).

== Conferences ==
* [http://www.recsys.acm.org] [[ACM RecSys]]
* [http://datamining.it.uts.edu.au/conferences/wi08/] ACM/IEEE Web Intelligence
* [http://www.aaai.org/Conferences/conferences.php] AAAI Conferences on Artificial Intelligence
* [http://www.ijcai.org/] IJCAI International Joint Conference on Artificial Intelligence
* [http://www.sigkdd.org/kdd2008/] ACM KDD Knowledge Discovery and Data Mining
* [http://www.sigir.org/events/events-upcoming.html] ACM SIGIR Information Retrieval Events
* [http://icdm08.isti.cnr.it/] IEEE Conference on Data Mining
* [http://wsdm2009.org/] ACM WSDM Web Search and Data Mining
* [http://www2009.org/] WWW International World Wide Web Conference
* [http://www.iuiconf.org/] IUI International Conference on Intelligent User Interfaces
* [http://www.enews.ece.uvic.ca/conf/MAW09/] IEEE MAW Symposium on Mining and Web
* [http://www.trustcomp.org/treck/] ACM SAC TRECK Trust, Reputation, Evidence and other Collaboration Know-how (ACM Syposium on Applied Computing)
* [http://www.dirf.org/diwt2009/] ICADIWT Applications of Digital Information and Web Technologies
* [http://www.sigecom.org/ec09/] ACM EC: Electronic Commerce
* [http://www.cikm2008.org/index.php] ACM Conference on Information and Knowledge Management
* [http://www.ht2009.org/] ACM SIGWEB Hypertext & Hypermedia
* [http://www.dexa.org/ecweb2011] Electronic Commerce and Web Technologies (EC-Web)
* [http://www.dia.uniroma3.it/~umap2013/] User Modelling, Personalisation, and Adaptation (UMAP)
* [http://www.cikm2011.org] ACM Conference on Information and Knowledge Management (CIKM)
* [http://ecir2012.upf.edu/] European Conference on Information Retrieval (ECIR)

== Journals ==
* ACM TOIS
* ACM TWEB
* ACM TIST
* [http://www.computer.org/portal/web/tkde/] IEEE Transactions on Knowledge and Data Engineering (TKDE)
* [http://www.springer.com/computer/database+management+&+information+retrieval/journal/11280] Spring World Wide Web Journal
* [http://www.springer.com/computer/information+systems+and+applications/journal/11042] Springer Multimedia Tools and Applications (MTAP)
* [http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10844] Springer Journal of Intelligent Information Systems (JIIS)

== Past Workshops ==
* At [http://recsys.acm.org/2010/ ACM RecSys 2010]
** [http://ir.ii.uam.es/prsat2010] Practical Use of Recommender Systems, Algorithms, & Technology
** [http://ir.ii.uam.es/hetrec2010] International Workshop on Information Heterogeneity and Fusion in Recommender Systems
** [http://adenu.ia.uned.es/workshops/recsystel2010/] Workshop on Recommender Systems for Technology Enhanced Learning
** [http://www.dcs.warwick.ac.uk/~ssanand/RSWEb.htm] 2nd Workshop on Recommender Systems and the Social Web
** [http://womrad.org/] Workshop on Music Recommendation and Discovery (WOMRAD)
** [http://ucersti.ieis.tue.nl/] User-Centric Evaluation of Recommender Systems and Their Interfaces
** [http://ids.csom.umn.edu/faculty/gedas/cars2010/] [http://www.dai-labor.de/camra2010/] 2nd Workshop on Context-Aware Recommender Systems & Challenge on Context-Aware Movie Recommendation
* [http://maya.cs.depaul.edu/~mobasher/itwp08/] Workshop on Intelligent Techniques for Web Personalization & Recommender Systems @ AAAI 2008
* [http://proserver3-iwas.uni-klu.ac.at/ECAI08-Recommender-Workshop/] Workshop on Recommender Systems @ ECAI 2008
* [http://netflixkddworkshop2008.info/] Workshop on Large-Scale Recommender Systems and the [[Netflix Prize]] Competition @ KDD 2008
* [http://userpages.uni-koblenz.de/~openconf/recoll/] International Workshop on Recommendation and Collaboration @ IUI 2008
* [http://www.dirf.org/diwt2008/workshop2.asp] First International Workshop on Recommender Systems and Personalized Retrieval @ ICADIWT 2008
* [http://widm2008.comp.nus.edu.sg/] International Workshop on Web Information and Data Management (WIDM)
* [http://ls13-www.cs.uni-dortmund.de/homepage/itwp2011] 9th Workshop on Intelligent Techniques for Web Personalisation (ITWP'11)

== Past Journal Special Editions/Book Chapters ==
* [http://tweb.acm.org/RecSysSpecialIssue.html] ACM TWEB Special Edition on Recommender Systems on the Web
* [http://www.springer.com/computer/ai/book/978-0-387-85819-7] Springer Recommender Systems Handbook
* [http://www.igi-global.com/reference/details.asp?ID=33031&v=tableOfContents] Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

[[Category: Event| ]]
[[Category: List|Conference]]

List of RecSys-relevant Conferences

2013-10-25T12:43:07Z

Sidooms: added two relevant springer journals

Looking for a place to publish your recommender system work?

'''Interests:'''

Web Personalisation, Recommender/Reputation Systems, User Modeling, Decision Support Systems, Artificial Intelligence, Data Mining, Data Clustering, Data Management, Scalability, Web 2.0, Social Network Analysis

'''Note:'''

The external links to these conferences often change (annually).

== Conferences ==
* [http://www.recsys.acm.org] [[ACM RecSys]]
* [http://datamining.it.uts.edu.au/conferences/wi08/] ACM/IEEE Web Intelligence
* [http://www.aaai.org/Conferences/conferences.php] AAAI Conferences on Artificial Intelligence
* [http://www.ijcai.org/] IJCAI International Joint Conference on Artificial Intelligence
* [http://www.sigkdd.org/kdd2008/] ACM KDD Knowledge Discovery and Data Mining
* [http://www.sigir.org/events/events-upcoming.html] ACM SIGIR Information Retrieval Events
* [http://icdm08.isti.cnr.it/] IEEE Conference on Data Mining
* [http://wsdm2009.org/] ACM WSDM Web Search and Data Mining
* [http://www2009.org/] WWW International World Wide Web Conference
* [http://www.iuiconf.org/] IUI International Conference on Intelligent User Interfaces
* [http://www.enews.ece.uvic.ca/conf/MAW09/] IEEE MAW Symposium on Mining and Web
* [http://www.trustcomp.org/treck/] ACM SAC TRECK Trust, Reputation, Evidence and other Collaboration Know-how (ACM Syposium on Applied Computing)
* [http://www.dirf.org/diwt2009/] ICADIWT Applications of Digital Information and Web Technologies
* [http://www.sigecom.org/ec09/] ACM EC: Electronic Commerce
* [http://www.cikm2008.org/index.php] ACM Conference on Information and Knowledge Management
* [http://www.ht2009.org/] ACM SIGWEB Hypertext & Hypermedia
* [http://www.dexa.org/ecweb2011] Electronic Commerce and Web Technologies (EC-Web)
* [http://www.dia.uniroma3.it/~umap2013/] User Modelling, Personalisation, and Adaptation (UMAP)
* [http://www.cikm2011.org] ACM Conference on Information and Knowledge Management (CIKM)
* [http://ecir2012.upf.edu/] European Conference on Information Retrieval (ECIR)

== Journals ==
* ACM TOIS
* ACM TWEB
* ACM TIST
* [http://www.computer.org/portal/web/tkde/] IEEE Transactions on Knowledge and Data Engineering (TKDE)
* [http://www.springer.com/computer/database+management+&+information+retrieval/journal/11280] Spring World Wide Web Journal
* [http://www.springer.com/computer/information+systems+and+applications/journal/11042] Springer Multimedia Tools and Applications
* [http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10844] Springer Journal of Intelligent Information Systems

== Past Workshops ==
* At [http://recsys.acm.org/2010/ ACM RecSys 2010]
** [http://ir.ii.uam.es/prsat2010] Practical Use of Recommender Systems, Algorithms, & Technology
** [http://ir.ii.uam.es/hetrec2010] International Workshop on Information Heterogeneity and Fusion in Recommender Systems
** [http://adenu.ia.uned.es/workshops/recsystel2010/] Workshop on Recommender Systems for Technology Enhanced Learning
** [http://www.dcs.warwick.ac.uk/~ssanand/RSWEb.htm] 2nd Workshop on Recommender Systems and the Social Web
** [http://womrad.org/] Workshop on Music Recommendation and Discovery (WOMRAD)
** [http://ucersti.ieis.tue.nl/] User-Centric Evaluation of Recommender Systems and Their Interfaces
** [http://ids.csom.umn.edu/faculty/gedas/cars2010/] [http://www.dai-labor.de/camra2010/] 2nd Workshop on Context-Aware Recommender Systems & Challenge on Context-Aware Movie Recommendation
* [http://maya.cs.depaul.edu/~mobasher/itwp08/] Workshop on Intelligent Techniques for Web Personalization & Recommender Systems @ AAAI 2008
* [http://proserver3-iwas.uni-klu.ac.at/ECAI08-Recommender-Workshop/] Workshop on Recommender Systems @ ECAI 2008
* [http://netflixkddworkshop2008.info/] Workshop on Large-Scale Recommender Systems and the [[Netflix Prize]] Competition @ KDD 2008
* [http://userpages.uni-koblenz.de/~openconf/recoll/] International Workshop on Recommendation and Collaboration @ IUI 2008
* [http://www.dirf.org/diwt2008/workshop2.asp] First International Workshop on Recommender Systems and Personalized Retrieval @ ICADIWT 2008
* [http://widm2008.comp.nus.edu.sg/] International Workshop on Web Information and Data Management (WIDM)
* [http://ls13-www.cs.uni-dortmund.de/homepage/itwp2011] 9th Workshop on Intelligent Techniques for Web Personalisation (ITWP'11)

== Past Journal Special Editions/Book Chapters ==
* [http://tweb.acm.org/RecSysSpecialIssue.html] ACM TWEB Special Edition on Recommender Systems on the Web
* [http://www.springer.com/computer/ai/book/978-0-387-85819-7] Springer Recommender Systems Handbook
* [http://www.igi-global.com/reference/details.asp?ID=33031&v=tableOfContents] Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

[[Category: Event| ]]
[[Category: List|Conference]]

Movietweetings

2013-08-20T12:28:07Z

Sidooms:

'''MovieTweetings''' is a dataset consisting of [[ratings]] on movies that were contained in well-structured tweets on Twitter.

== The Goal ==
The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. While the typical datasets as Netflix, [[MovieLens]], etc. are still popular in research, they are losing their relevancy as time goes by. The MovieTweetings dataset offers ratings on popular and contemporary movies, which can be useful for user-centric experiments and live demos of recommender systems.

The dataset will be updated as much as possible to incorporate rating data from the newest tweets available. Note however that the system relies on the continuation of the IMDb apps and the Twitter API.

== The Numbers ==

The earliest rating contained in this dataset is from 28 Feb 2013, since then all relevant tweets have been processed and added to the dataset, which (at the time of writing) results in the following numbers:
*91,306 ratings
*15,164 users
*10,012 movies

Note that this is a natural dataset, meaning that there has been no user filtering. While datasets as MovieLens often exclude users that have rated under 20 movies, here users are included as soon as they have rated at least 1 movie (i.e., have tweeted about at least 1 movie). As of a result, the sparsity for the MovieTweetings dataset will be higher than that of filtered datasets.

== Ratings from Twitter ==

This dataset consists of ratings extracted from tweets. To be able to correctly extract the ratings, only well-structured tweets are taken into account. The best source available for this, is the social rating widget available in IMDb apps. While rating movies, in these apps, a well-structured tweet is proposed to the user of the form:

"<nowiki>I rated The Matrix 9/10 http://www.imdb.com/title/tt0133093/ #IMDb</nowiki>"

On a daily basis the Twitter API is queried for the term "I rated #IMDb" and the resulting tweets are processed and integrated in the dataset.

The numeric IMDb identifier was adopted as item id to facilitate additional metadata enrichment and guarantee movie uniqueness. For example, for the above tweet the item id would be "0133093" which allows to infer the corresponding IMDb page link (add <nowiki>http://www.imdb.com/title/tt</nowiki>). The user id simply ranges from 1 to 'the number of users'.

== The Dataset ==

The dataset is still growing and so it offers two views on the data: all the data, and snapshots. The snapshots contain fixed (chronologically) portions of the dataset to allow experimentation and reproducibility of research.

The dataset files are modeled after the MovieLens dataset to make them as interchangeable as possible. There are two files: '''items.dat''' and '''ratings.dat'''.

=== items.dat ===

Contains the items (i.e., movies) that were rated in the tweets, together with their genre metadata in the following format: movie_id::movie_title (movie_year)::genre|genre|genre. For example:

0110912::Pulp Fiction (1994)::Crime|Thriller

The file is UTF-8 encoded to deal with the many foreign movie titles contained in tweets.

=== ratings.dat ===

In this file, the extracted ratings are stored in the following format: user_id::movie_id::rating::rating_timestamp. For example:

14927::0110912::9::1375657563

The rating values contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform.

== Literature ==

The corresponding paper will be presented at the CrowdRec workshop which is co-located with the ACM RecSys 2013 conference.

"MovieTweetings: a Movie Rating Dataset Collected From Twitter"

== External Links ==
* <nowiki>https://github.com/sidooms/MovieTweetings</nowiki>
* <nowiki>http://crowdrec2013.noahlab.com.hk</nowiki>

[[Category: Dataset]]
[[Category: Movie recommendation]]

Movietweetings

2013-08-20T12:20:51Z

Sidooms:

Movietweetings

2013-08-20T12:19:39Z

Sidooms:

'''MovieTweetings''' is a dataset consisting of [[ratings]] on movies that were contained in well-structured tweets on Twitter.

== The Goal ==
The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. While the typical datasets as Netflix, [[MovieLens]], etc. are still popular in research, they are losing their relevancy as time goes by. The MovieTweetings dataset offers ratings on popular and contemporary movies, which can be useful for user-centric experiments and live demos of recommender systems.

The dataset will be updated as much as possible to incorporate rating data from the newest tweets available. Note however that the system relies on the continuation of the IMDb apps and the Twitter API.

== The Numbers ==

The earliest rating contained in this dataset is from 28 Feb 2013, since then all relevant tweets have been processed and added to the dataset, which (at the time of writing) results in the following numbers:
*91,306 ratings
*15,164 users
*10,012 movies

Note that this is a natural dataset, meaning that there has been no user filtering. While datasets as MovieLens often exclude users that have rated under 20 movies, here users are included as soon as they have rated at least 1 movie (i.e., have tweeted about at least 1 movie). As of a result, the sparsity for the MovieTweetings dataset will be higher than that of filtered datasets.

== Ratings from Twitter ==

This dataset consists of ratings extracted from tweets. To be able to correctly extract the ratings, only well-structured tweets are taken into account. The best source available for this, is the social rating widget available in IMDb apps. While rating movies, in these apps, a well-structured tweet is proposed to the user of the form:

"<nowiki>I rated The Matrix 9/10 http://www.imdb.com/title/tt0133093/ #IMDb</nowiki>"

On a daily basis the Twitter API is queried for the term "I rated #IMDb" and the resulting tweets are processed and integrated in the dataset.

The numeric IMDb identifier was adopted as item id to facilitate additional metadata enrichment and guarantee movie uniqueness. For example, for the above tweet the item id would be "0133093" which allows to infer the corresponding IMDb page link (add thtp://www.imdb.com/title/tt). The user id simply ranges from 1 to 'the number of users'.

== The Dataset ==

The dataset is still growing and so it offers two views on the data: all the data, and snapshots. The snapshots contain fixed (chronologically) portions of the dataset to allow experimentation and reproducibility of research.

The dataset files are modeled after the MovieLens dataset to make them as interchangeable as possible. There are two files: '''items.dat''' and '''ratings.dat'''.

=== items.dat ===

Contains the items (i.e., movies) that were rated in the tweets, together with their genre metadata in the following format: movie_id::movie_title (movie_year)::genre|genre|genre. For example:

0110912::Pulp Fiction (1994)::Crime|Thriller

The file is UTF-8 encoded to deal with the many foreign movie titles contained in tweets.

=== ratings.dat ===

In this file, the extracted ratings are stored in the following format: user_id::movie_id::rating::rating_timestamp. For example:

14927::0110912::9::1375657563

The rating values contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform.

== Literature ==

The corresponding paper will be presented at the CrowdRec workshop which is co-located with the ACM RecSys 2013 conference.

"MovieTweetings: a Movie Rating Dataset Collected From Twitter"

== External Links ==
...

Movietweetings

2013-08-20T12:17:05Z

Sidooms:

'''MovieTweetings''' is a dataset consisting of [[ratings]] on movies that were contained in well-structured tweets on Twitter.

== The Goal ==
The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. While the typical datasets as Netflix, [[MovieLens]], etc. are still popular in research, they are losing their relevancy as time goes by. The MovieTweetings dataset offers ratings on popular and contemporary movies, which can be useful for user-centric experiments and live demos of recommender systems.

The dataset will be updated as much as possible to incorporate rating data from the newest tweets available. Note however that the system relies on the continuation of the IMDb apps and the Twitter API.

== The Numbers ==

The earliest rating contained in this dataset is from 28 Feb 2013, since then all relevant tweets have been processed and added to the dataset, which (at the time of writing) results in the following numbers:
*91,306 ratings
*15,164 users
*10,012 movies

Note that this is a natural dataset, meaning that there has been no user filtering. While datasets as MovieLens often exclude users that have rated under 20 movies, here users are included as soon as they have rated at least 1 movie (i.e., have tweeted about at least 1 movie). As of a result, the sparsity for the MovieTweetings dataset will be higher than that of filtered datasets.

== Ratings from Twitter ==

This dataset consists of ratings extracted from tweets. To be able to correctly extract the ratings, only well-structured tweets are taken into account. The best source available for this, is the social rating widget available in IMDb apps. While rating movies, in these apps, a well-structured tweet is proposed to the user of the form:

"I rated The Matrix 9/10 thtp://www.imdb.com/title/tt0133093/ #IMDb"

On a daily basis the Twitter API is queried for the term "I rated #IMDb" and the resulting tweets are processed and integrated in the dataset.

The numeric IMDb identifier was adopted as item id to facilitate additional metadata enrichment and guarantee movie uniqueness. For example, for the above tweet the item id would be "0133093" which allows to infer the corresponding IMDb page link (add thtp://www.imdb.com/title/tt). The user id simply ranges from 1 to 'the number of users'.

== The Dataset ==

The dataset is still growing and so it offers two views on the data: all the data, and snapshots. The snapshots contain fixed (chronologically) portions of the dataset to allow experimentation and reproducibility of research.

The dataset files are modeled after the MovieLens dataset to make them as interchangeable as possible. There are two files: '''items.dat''' and '''ratings.dat'''.

=== items.dat ===

Contains the items (i.e., movies) that were rated in the tweets, together with their genre metadata in the following format: movie_id::movie_title (movie_year)::genre|genre|genre. For example:

0110912::Pulp Fiction (1994)::Crime|Thriller

The file is UTF-8 encoded to deal with the many foreign movie titles contained in tweets.

=== ratings.dat ===

In this file, the extracted ratings are stored in the following format: user_id::movie_id::rating::rating_timestamp. For example:

14927::0110912::9::1375657563

The rating values contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform.

== Literature ==

The corresponding paper will be presented at the CrowdRec workshop which is co-located with the ACM RecSys 2013 conference.

"MovieTweetings: a Movie Rating Dataset Collected From Twitter"

== External Links ==
...

Movietweetings

2013-08-20T12:14:15Z

Sidooms:

Movietweetings

2013-08-20T12:13:18Z

Sidooms: added information about the MovieTweetings dataset

'''MovieTweetings''' is a dataset consisting of ratings on movies that were contained in well-structured tweets on Twitter.

== The Goal ==
The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. While the typical datasets as Netflix, MovieLens, etc. are still popular in research, they are losing their relevancy as time goes by. The MovieTweetings dataset offers ratings on popular and contemporary movies, which can be useful for user-centric experiments and live demos of recommender systems.

The dataset will be updated as much as possible to incorporate rating data from the newest tweets available. Note however that the system relies on the continuation of the IMDb apps and the Twitter API.

== The Numbers ==

The earliest rating contained in this dataset is from 28 Feb 2013, since then all relevant tweets have been processed and added to the dataset, which (at the time of writing) results in the following numbers:
*91,306 ratings
*15,164 users
*10,012 movies

Note that this is a natural dataset, meaning that there has been no user filtering. While datasets as MovieLens often exclude users that have rated under 20 movies, here users are included as soon as they have rated at least 1 movie (i.e., have tweeted about at least 1 movie). As of a result, the sparsity for the MovieTweetings dataset will be higher than that of filtered datasets.

== Ratings from Twitter ==

This dataset consists of ratings extracted from tweets. To be able to correctly extract the ratings, only well-structured tweets are taken into account. The best source available for this, is the social rating widget available in IMDb apps. While rating movies, in these apps, a well-structured tweet is proposed to the user of the form:

"I rated The Matrix 9/10 thtp://www.imdb.com/title/tt0133093/ #IMDb"

On a daily basis the Twitter API is queried for the term "I rated #IMDb" and the resulting tweets are processed and integrated in the dataset.

The numeric IMDb identifier was adopted as item id to facilitate additional metadata enrichment and guarantee movie uniqueness. For example, for the above tweet the item id would be "0133093" which allows to infer the corresponding IMDb page link (add thtp://www.imdb.com/title/tt). The user id simply ranges from 1 to 'the number of users'.

== The Dataset ==

The dataset is still growing and so it offers two views on the data: all the data, and snapshots. The snapshots contain fixed (chronologically) portions of the dataset to allow experimentation and reproducibility of research.

The dataset files are modeled after the MovieLens dataset to make them as interchangeable as possible. There are two files: '''items.dat''' and '''ratings.dat'''.

=== items.dat ===

Contains the items (i.e., movies) that were rated in the tweets, together with their genre metadata in the following format: movie_id::movie_title (movie_year)::genre|genre|genre. For example:

0110912::Pulp Fiction (1994)::Crime|Thriller

The file is UTF-8 encoded to deal with the many foreign movie titles contained in tweets.

=== ratings.dat ===

In this file, the extracted ratings are stored in the following format: user_id::movie_id::rating::rating_timestamp. For example:

14927::0110912::9::1375657563

The rating values contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform.

== Literature ==

The corresponding paper will be presented at the CrowdRec workshop which is co-located with the ACM RecSys 2013 conference.

"MovieTweetings: a Movie Rating Dataset Collected From Twitter"

== External Links ==
'''MovieTweetings''' is a dataset consisting of ratings on movies that were contained in well-structured tweets on Twitter.

== The Goal ==
The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. While the typical datasets as Netflix, MovieLens, etc. are still popular in research, they are losing their relevancy as time goes by. The MovieTweetings dataset offers ratings on popular and contemporary movies, which can be useful for user-centric experiments and live demos of recommender systems.

The dataset will be updated as much as possible to incorporate rating data from the newest tweets available. Note however that the system relies on the continuation of the IMDb apps and the Twitter API.

== The Numbers ==

The earliest rating contained in this dataset is from 28 Feb 2013, since then all relevant tweets have been processed and added to the dataset, which (at the time of writing) results in the following numbers:
*91,306 ratings
*15,164 users
*10,012 movies

Note that this is a natural dataset, meaning that there has been no user filtering. While datasets as MovieLens often exclude users that have rated under 20 movies, here users are included as soon as they have rated at least 1 movie (i.e., have tweeted about at least 1 movie). As of a result, the sparsity for the MovieTweetings dataset will be higher than that of filtered datasets.

== Ratings from Twitter ==

This dataset consists of ratings extracted from tweets. To be able to correctly extract the ratings, only well-structured tweets are taken into account. The best source available for this, is the social rating widget available in IMDb apps. While rating movies, in these apps, a well-structured tweet is proposed to the user of the form:

"I rated The Matrix 9/10 thtp://www.imdb.com/title/tt0133093/ #IMDb"

On a daily basis the Twitter API is queried for the term "I rated #IMDb" and the resulting tweets are processed and integrated in the dataset.

The numeric IMDb identifier was adopted as item id to facilitate additional metadata enrichment and guarantee movie uniqueness. For example, for the above tweet the item id would be "0133093" which allows to infer the corresponding IMDb page link (add thtp://www.imdb.com/title/tt). The user id simply ranges from 1 to 'the number of users'.

== The Dataset ==

The dataset is still growing and so it offers two views on the data: all the data, and snapshots. The snapshots contain fixed (chronologically) portions of the dataset to allow experimentation and reproducibility of research.

The dataset files are modeled after the MovieLens dataset to make them as interchangeable as possible. There are two files: '''items.dat''' and '''ratings.dat'''.

=== items.dat ===

Contains the items (i.e., movies) that were rated in the tweets, together with their genre metadata in the following format: movie_id::movie_title (movie_year)::genre|genre|genre. For example:

0110912::Pulp Fiction (1994)::Crime|Thriller

The file is UTF-8 encoded to deal with the many foreign movie titles contained in tweets.

=== ratings.dat ===

In this file, the extracted ratings are stored in the following format: user_id::movie_id::rating::rating_timestamp. For example:

14927::0110912::9::1375657563

The rating values contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform.

== Literature ==

The corresponding paper will be presented at the CrowdRec workshop which is co-located with the ACM RecSys 2013 conference.

"MovieTweetings: a Movie Rating Dataset Collected From Twitter"

== External Links ==
* thtps://github.com/sidooms/MovieTweetings
* thtp://crowdrec2013.noahlab.com.hk

List of recommender system dissertations

2012-01-25T14:12:33Z

Sidooms: /* 2010 */

Recommender systems-related dissertations by year.

Dissertation within a year are sorted alphabetically by title.

=== 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.aka-verlag.com/de/detail?ean=978-3-89838-332-5 Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems] - [[Robert Jäschke]]
* [http://slavnik.fe.uni-lj.si/markot/uploads/Main/2010_tkalcic_phd.pdf Recognition and usage of emotive parameters in recommender systems] - [[Marko Tkalčič]]

=== 2010 ===
* [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]]
* [https://biblio.ugent.be/publication/986279/file/1886805.pdf Trust networks for recommender systems] - [[Patricia Victor]]

=== 2009 ===
* [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://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://arantxa.ii.uam.es/~cantador/doc/2008/thesis08.zip Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach] - [[Iván Cantador]]
* [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]]
* 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]]
* 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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]
* [http://www.ulrichpaquet.com/Papers/PhDThesis.pdf Bayesian Inference for Latent Variable Models] - [[Ulrich Paquet]]

=== 2006 ===
* [http://www-users.cs.umn.edu/~mcnee/mcnee-thesis-preprint.pdf Meeting User Information Needs in Recommender Systems] - [[Sean McNee]]

=== 2005 ===
* [http://dl.acm.org/citation.cfm?id=1123874 Explanet: a learning tool and hybrid recommender system for student-authored explanations] - [[Jessica Masters]]
* [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]]
* [http://knuth.luther.edu/~bmiller/Papers/thesis.pdf Toward a Personal Recommender System] - [[Bradley N. Miller]]

=== 2002 ===

=== 2001 ===
* [http://www.patrickbaudisch.com/publications/2001-Baudisch-Dissertation-DynamicInformationFiltering.pdf Dynamic Information Filtering] - [[Patrick Baudisch]]
* [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]

[[Category: List|Dissertation]]