List of recommender system dissertations

2012-08-16T17:30:09Z

Bmcfee: /* 2012 */ added bmcfee's dissertation

[[Recommender systems]] related dissertations by year.

Dissertation within a year are sorted alphabetically by title.

=== 2012 ===
* [http://opus.bsz-bw.de/ubhi/volltexte/2012/167/pdf/item_recommendation.pdf Supervised Machine Learning Methods for Item Recommendation] - [[Zeno Gantner]]
* [http://library.epfl.ch/en/theses/?nr=5318 Design and User Perception Issues for Personality-Engaged Recommender Systems] - [[Rong Hu]]
* [http://cseweb.ucsd.edu/~bmcfee/papers/bmcfee_dissertation.pdf More like this: machine learning approaches to music similarity] - [[Brian McFee]]

=== 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]]
* [http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/1687272 Using Data Mining for Facilitating User Contributions in the Social Semantic Web] - [[Maryam Ramezani]]

=== 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]]
* [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]]
* 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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2714&rep=rep1&type=pdf Building Trustworthy Recommender Systems] - [[Sheng Zhang]]

=== 2006 ===
* [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]]
* [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]]

Million Song Dataset Challenge

2012-07-11T16:40:29Z

Bmcfee: Adding a page for the MSD challenge

'''DEADLINE: 11:59 pm, Thursday 9 August 2012 UTC'''

The [http://www.kaggle.com/c/msdchallenge Million Song Dataset Challenge] aims at being the best possible offline evaluation of a music recommendation system. Any type of algorithm can be used: collaborative filtering, content-based methods, web crawling, even human oracles!* By relying on the [http://labrosa.ee.columbia.edu/millionsong/ Million Song Dataset], the data for the competition is completely open: almost everything is known and possibly available.

What is the task in a few words? You have: 1) the full listening history for 1M users, 2) half of the listening history for 110K users (10K validation set, 100K test set), and you must predict the missing half. How much easier can it get?

The most straightforward approach to this task is pure collaborative filtering, but remember that there is a wealth of information available to you through the Million Song Dataset. Go ahead, explore! If you have questions, we recommend that you consult the MSD Mailing List.

Ready to start recommending? Read through our [https://kaggle2.blob.core.windows.net/competitions/kaggle/2799/media/MSDChallengeGettingstarted.pdf Getting Started] tutorial.

For a more technical introduction to the MSD Challenge, see our [http://www.columbia.edu/~tb2332/Papers/admire12.pdf AdMIRe] paper. (Please use this following [http://www.columbia.edu/~tb2332/Papers/admire12.bib citation] when referring to the contest in an academic setting.)

* This contest is for computer models, but if you manage to get recommendations from humans for 110K listeners, we'd like to know how!

The Million Song Dataset Challenge is a joint effort between the [http://cosmal.ucsd.edu/cal/ Computer Audition Lab] at [http://www.ucsd.edu/ UC San Diego] and [http://labrosa.ee.columbia.edu/ LabROSA] at [http://www.columbia.edu/ Columbia University]. The user data for the challenge, like much of the data in the Million Song Dataset, was generously donated by [http://the.echonest.com/ The Echo Nest], with additional data contributed by [http://www.secondhandsongs.com/ SecondHandSongs], [http://musixmatch.com/ musiXmatch], and [http://www.last.fm/ Last.fm]. Follow-up evaluations will be conducted by [http://www.music-ir.org/ IMIRSEL] at the [http://www.lis.illinois.edu/ Graduate School of Library Information Science at UIUC] as part of the Music Information Retrieval Evaluation eXchange ([http://music-ir.org/mirex/wiki/MIREX_HOME MIREX]).

[[Category:Competition]]

RecSysWiki - User contributions [en]

List of recommender system dissertations

Million Song Dataset Challenge