Recommender systems are now popular both commercially and in the research p@20: community, where many approaches have been suggested for providing p@20: recommendations. In many cases a system designer that wishes to employ a p@20: recommendation system must choose between a set of candidate approaches. A first p@20: step towards selecting an appropriate algorithm is to decide which properties of p@20: the application to focus upon when making this choice. Indeed, recommendation p@20: systems have a variety of properties that may affect user experience, such as p@20: accuracy, robustness, scalability, and so forth. In this paper we discuss how to p@20: compare recommenders based on a set of properties that are relevant for e p@20: application. We focus on comparative studies, where a few algorithms are compared p@20: using some evaluation metric, rather than absolute benchmarking of algorithms. We p@20: describe experimental settings appropriate for making choices between algorithms. p@20: We review three types of experiments, starting with an offline setting, where p@20: recommendation approaches are compared without user interaction, then reviewing p@20: user studies, where a small group of subjects experiment with the system and p@20: report on the experience, and finally describe large scale online experiments, p@20: where real user populations interact with the system. In each of these cases we p@20: describe types of questions that can be answered, and suggest protocols for p@20: experimentation. We also discuss how to draw trustworthy conclusions from e p@20: conducted experiments. We then review a large set of properties, and explain how p@20: to evaluate systems given relevant properties. We also survey a large set of p@20: evaluation metrics in the context of the property that they evaluate.
}, p@20: author = {Guy Shani and Asela Gunawardana}, p@20: month = {November}, p@20: number = {MSR-TR-2009-159}, p@20: publisher = {Microsoft Research}, p@20: title = {Evaluating Recommender Systems}, p@20: url = {http://research.microsoft.com/apps/pubs/default.aspx?id=115396}, p@20: year = {2009}, p@20: } p@20: p@20: @phdthesis {1242, p@20: title = {Music Recommendation and Discovery in the Long Tail}, p@20: year = {2008}, p@20: school = {Universitat Pompeu Fabra}, p@20: address = {Barcelona}, p@20: abstract = {p@20: Music consumption is biased towards a few popular artists. For instance, in 2007 only 1\% of p@20: all digital tracks accounted for 80\% of all sales. Similarly, 1,000 albums accounted for 50\% p@20: of all album sales, and 80\% of all albums sold were purchased less than 100 times. There is p@20: a need to assist people to filter, discover, personalise and recommend from the huge amount p@20: of music content available along the Long Tail. p@20:
p@20:p@20: Current music recommendation algorithms try to p@20: accurately predict what people demand to listen to. However, quite p@20: often these algorithms tend to recommend popular -or well-known to the p@20: user- music, decreasing the effectiveness of the recommendations. These p@20: approaches focus on improving the accuracy of the recommendations. That p@20: is, try to make p@20: accurate predictions about what a user could listen to, or buy next, p@20: independently of how p@20: useful to the user could be the provided recommendations. p@20:
p@20:p@20: In this Thesis we stress the importance of the user{\textquoteright}s p@20: perceived quality of the recommendations. We model the Long Tail curve p@20: of artist popularity to predict -potentially- p@20: interesting and unknown music, hidden in the tail of the popularity p@20: curve. Effective recommendation systems should promote novel and p@20: relevant material (non-obvious recommendations), taken primarily from p@20: the tail of a popularity distribution. p@20:
p@20:p@20: The main contributions of this Thesis are: (i) a novel network-based approach for p@20: recommender systems, based on the analysis of the item (or user) similarity graph, and the p@20: popularity of the items, (ii) a user-centric evaluation that measures the user{\textquoteright}s relevance p@20: and novelty of the recommendations, and (iii) two prototype systems that implement the p@20: ideas derived from the theoretical work. Our findings have significant implications for p@20: recommender systems that assist users to explore the Long Tail, digging for content they p@20: might like. p@20:
p@20: }, p@20: url = {http://mtg.upf.edu/static/media/PhD\_ocelma.pdf}, p@20: author = {Celma, \{`O}.}, p@20: } p@21: p@21: @inproceedings{pachet2001musical, p@21: title={Musical data mining for electronic music distribution}, p@21: author={Pachet, Fran{\c{c}}ois and Westermann, Gert and Laigre, Damien}, p@21: booktitle={Web Delivering of Music, 2001. Proceedings. First International Conference on}, p@21: pages={101--106}, p@21: year={2001}, p@21: organization={IEEE} p@21: } p@21: p@21: @ARTICLE{Tzanetakis2002293, p@21: author={Tzanetakis, G. and Cook, P.}, p@21: title={Musical genre classification of audio signals}, p@21: journal={IEEE Transactions on Speech and Audio Processing}, p@21: year={2002}, p@21: volume={10}, p@21: number={5}, p@21: pages={293-302}, p@21: doi={10.1109/TSA.2002.800560}, p@21: note={cited By 976}, p@21: url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0036648502\&partnerID=40\&md5=72d2fee186b42c9998f13415cbb79eea}, p@21: document_type={Article}, p@21: source={Scopus}, p@21: } p@21: p@21: @CONFERENCE{Sturm20127, p@21: author={Sturm, B.L.}, p@21: title={An analysis of the GTZAN music genre dataset}, p@21: journal={MIRUM 2012 - Proceedings of the 2nd International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, Co-located with ACM Multimedia 2012}, p@21: year={2012}, p@21: pages={7-12}, p@21: doi={10.1145/2390848.2390851}, p@21: note={cited By 0}, p@21: url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84870497334\&partnerID=40\&md5=40a48c1c9d787308dd315694b54b64ec}, p@21: document_type={Conference Paper}, p@21: source={Scopus}, p@21: } p@21: p@21: @unpublished{Bengio-et-al-2015-Book, p@21: title={Deep Learning}, p@21: author={Yoshua Bengio and Ian J. Goodfellow and Aaron Courville}, p@21: note={Book in preparation for MIT Press}, p@21: url={http://www.iro.umontreal.ca/~bengioy/dlbook}, p@21: year={2015} p@21: } p@21: p@21: @CONFERENCE{Sigtia20146959, p@21: author={Sigtia, S. and Dixon, S.}, p@21: title={Improved music feature learning with deep neural networks}, p@21: journal={ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, p@21: year={2014}, p@21: pages={6959-6963}, p@21: doi={10.1109/ICASSP.2014.6854949}, p@21: art_number={6854949}, p@21: note={cited By 0}, p@21: url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84905259152\&partnerID=40\&md5=3441dfa8c7998a8eb39f668d43efb8a1}, p@21: document_type={Conference Paper}, p@21: source={Scopus}, p@21: } p@21: p@21: @incollection{NIPS2013_5004, p@21: title = {Deep content-based music recommendation}, p@21: author = {van den Oord, Aaron and Dieleman, Sander and Schrauwen, Benjamin}, p@21: booktitle = {Advances in Neural Information Processing Systems 26}, p@21: editor = {C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger}, p@21: pages = {2643--2651}, p@21: year = {2013}, p@21: publisher = {Curran Associates, Inc.}, p@21: url = {http://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf} p@21: } p@21: p@21: @incollection{pelikan2015estimation, p@21: title={Estimation of Distribution Algorithms}, p@21: author={Pelikan, Martin and Hauschild, Mark W and Lobo, Fernando G}, p@21: booktitle={Springer Handbook of Computational Intelligence}, p@21: pages={899--928}, p@21: year={2015}, p@21: publisher={Springer} p@21: } p@21: p@21: @article{Santana:Bielza:Larrañaga:Lozano:Echegoyen:Mendiburu:Armañanzas:Shakya:2009:JSSOBK:v35i07, p@21: author = "Roberto Santana and Concha Bielza and Pedro Larrañaga and Jose A. Lozano and Carlos Echegoyen and Alexander Mendiburu and Rubén Armañanzas and Siddartha Shakya", p@21: title = "Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms", p@21: journal = "Journal of Statistical Software", p@21: volume = "35", p@21: number = "7", p@21: pages = "1--30", p@21: day = "26", p@21: month = "7", p@21: year = "2010", p@21: CODEN = "JSSOBK", p@21: ISSN = "1548-7660", p@21: bibdate = "2009-12-17", p@21: URL = "http://www.jstatsoft.org/v35/i07", p@21: accepted = "2009-12-17", p@21: acknowledgement = "", p@21: keywords = "", p@21: submitted = "2009-04-15", p@21: } p@21: p@21: @ARTICLE{Liang2014781, p@21: author={Liang, T. and Liang, Y. and Fan, J. and Zhao, J.}, p@21: title={A hybrid recommendation model based on estimation of distribution algorithms}, p@21: journal={Journal of Computational Information Systems}, p@21: year={2014}, p@21: volume={10}, p@21: number={2}, p@21: pages={781-788}, p@21: doi={10.12733/jcis9623}, p@21: note={cited By 0}, p@21: url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84892865461\&partnerID=40\&md5=a2927d36b493e8ef4d1cdab3055fa68b}, p@21: document_type={Article}, p@21: source={Scopus}, p@21: } p@21: p@21: @INPROCEEDINGS{Bertin-Mahieux2011, p@21: author = {Thierry Bertin-Mahieux and Daniel P.W. Ellis and Brian Whitman and Paul Lamere}, p@21: title = {The Million Song Dataset}, p@21: booktitle = {{Proceedings of the 12th International Conference on Music Information p@21: Retrieval ({ISMIR} 2011)}}, p@21: year = {2011}, p@21: owner = {thierry}, p@21: timestamp = {2010.03.07} p@21: } p@21: p@21: @inproceedings{zhang2014deep, p@21: title={A deep representation for invariance and music classification}, p@21: author={Zhang, Chiyuan and Evangelopoulos, Georgios and Voinea, Stephen and Rosasco, Lorenzo and Poggio, Tomaso}, p@21: booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on}, p@21: pages={6984--6988}, p@21: year={2014}, p@21: organization={IEEE} p@21: }