Advanced Factorization Models for Recommender Systems

Recommender Systems have become a crucial tool to serve personalized content and to promote online products and media, but also to recommend restaurants, events, news and dating profiles. The underlying algorithms have a significant impact on the quality of recommendations and have been the subject of many studies in the last two decades. In this thesis we focus on factorization models, a class of recommender system algorithms that learn user preferences based on a method called factorization. This method is a common approach in Collaborative Filtering (CF), the most successful and widely-used technique in recommender systems, where user preferences are learnt based on the preferences of similar users. We study factorization models from an algorithmic perspective to be able to extend their applications to a wider range of problems and to improve their effectiveness. The majority of the techniques that are proposed in this thesis are based on state-of-the-art factorization models known as Factorization Machines (FMs).

[1]  Bao-Gang Hu,et al.  Linear feature-weighted support vector machine , 2009 .

[2]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[3]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[4]  Mirko Polato,et al.  Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning , 2017, ICANN.

[5]  Martha Larson,et al.  Factorization Machines for Data with Implicit Feedback , 2018, ArXiv.

[6]  Lars Schmidt-Thieme,et al.  Bayesian Personalized Ranking for Non-Uniformly Sampled Items , 2011 .

[7]  Luis M. de Campos,et al.  Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks , 2010, Int. J. Approx. Reason..

[8]  Harald Steck,et al.  Training and testing of recommender systems on data missing not at random , 2010, KDD.

[9]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[10]  Guandong Xu,et al.  Personalized recommendation via cross-domain triadic factorization , 2013, WWW.

[11]  CARLOS A. GOMEZ-URIBE,et al.  The Netflix Recommender System , 2015, ACM Trans. Manag. Inf. Syst..

[12]  Alexandros Karatzoglou,et al.  Gaussian process factorization machines for context-aware recommendations , 2014, SIGIR.

[13]  Hendrik Schreiber,et al.  Improving Genre Annotations for the Million Song Dataset , 2015, ISMIR.

[14]  Steffen Rendle,et al.  Improving pairwise learning for item recommendation from implicit feedback , 2014, WSDM.

[15]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[16]  Robert A. Legenstein,et al.  Combining predictions for accurate recommender systems , 2010, KDD.

[17]  Max Welling,et al.  Bayesian Matrix Factorization with Side Information and Dirichlet Process Mixtures , 2010, AAAI.

[18]  Xiaobo Zhou,et al.  Please spread: recommending tweets for retweeting with implicit feedback , 2012, DUBMMSM '12.

[19]  Steffen Rendle.  Scaling Factorization Machines to Relational Data , 2013, Proc. VLDB Endow..

[20]  Alan Said,et al.  Comparative recommender system evaluation: benchmarking recommendation frameworks , 2014, RecSys '14.

[21]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[22]  Julian J. McAuley,et al.  VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback , 2015, AAAI.

[23]  Francesco Ricci,et al.  A survey of active learning in collaborative filtering recommender systems , 2016, Comput. Sci. Rev..

[24]  Martha Larson,et al.  xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance , 2013, RecSys.

[25]  Lars Schmidt-Thieme,et al.  MyMediaLite: a free recommender system library , 2011, RecSys '11.

[26]  Martha Larson,et al.  Cross-Domain Collaborative Filtering with Factorization Machines , 2014, ECIR.

[27]  Martha Larson,et al.  Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines , 2019, ACM Trans. Inf. Syst..

[28]  Brian Whitman,et al.  Music Personalization at Spotify , 2016, RecSys.

[29]  Marcelo G. Manzato,et al.  Exploiting multimodal interactions in recommender systems with ensemble algorithms , 2016, Inf. Syst..

[30]  Lars Schmidt-Thieme,et al.  Fast context-aware recommendations with factorization machines , 2011, SIGIR.

[31]  Steffen Rendle,et al.  Factorization Machines with libFM , 2012, TIST.

[32]  Alexandros Karatzoglou,et al.  Collaborative temporal order modeling , 2011, RecSys '11.

[33]  Qiang Yang,et al.  Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction , 2009, IJCAI.

[34]  Qiang Chen,et al.  Exploiting Explicit and Implicit Feedback for Personalized Ranking , 2016 .

[35]  Suhrid Balakrishnan,et al.  Collaborative ranking , 2012, WSDM '12.

[36]  Massih-Reza Amini,et al.  Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems , 2017, ArXiv.

[37]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[38]  Byeong Man Kim,et al.  Clustering approach for hybrid recommender system , 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003).

[39]  Thierry Bertin-Mahieux,et al.  The Million Song Dataset , 2011, ISMIR.

[40]  Nuria Oliver,et al.  Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild , 2015, ArXiv.

[41]  Martha Larson,et al.  Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..

[42]  Brian D. Davison,et al.  Co-factorization machines: modeling user interests and predicting individual decisions in Twitter , 2013, WSDM.

[43]  Lars Schmidt-Thieme,et al.  Learning Attribute-to-Feature Mappings for Cold-Start Recommendations , 2010, 2010 IEEE International Conference on Data Mining.

[44]  Martha Larson,et al.  CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering , 2012, RecSys.

[45]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[46]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[47]  Alan Said,et al.  WrapRec: an easy extension of recommender system libraries , 2014, RecSys '14.

[48]  Tat-Seng Chua,et al.  Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks , 2017, IJCAI.

[49]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[50]  Mingxuan Sun,et al.  A Comparative Study of Collaborative Filtering Algorithms , 2012, Proceedings of the International Conference on Knowledge Discovery and Information Retrieval.

[51]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[52]  Martin Szomszor,et al.  Comparison of implicit and explicit feedback from an online music recommendation service , 2010, HetRec '10.

[53]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[54]  Martha Larson,et al.  Recommendation with the Right Slice: Speeding Up Collaborative Filtering with Factorization Machines , 2015, RecSys Posters.

[55]  Dietmar Jannach,et al.  Using graded implicit feedback for bayesian personalized ranking , 2014, RecSys '14.

[56]  Naonori Ueda,et al.  Higher-Order Factorization Machines , 2016, NIPS.

[57]  Yijun Sun,et al.  Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[58]  Steffen Rendle,et al.  Context-Aware Ranking with Factorization Models , 2010, Studies in Computational Intelligence.

[59]  MengChu Zhou,et al.  An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems , 2014, IEEE Transactions on Industrial Informatics.

[60]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[61]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

[62]  Martha Larson,et al.  Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation , 2013, Inf. Sci..

[63]  Markus Zanker,et al.  Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[64]  Martha Larson,et al.  TFMAP: optimizing MAP for top-n context-aware recommendation , 2012, SIGIR '12.

[65]  Chih-Jen Lin,et al.  Field-aware Factorization Machines for CTR Prediction , 2016, RecSys.

[66]  Alejandro Bellogín,et al.  Precision-oriented evaluation of recommender systems: an algorithmic comparison , 2011, RecSys '11.

[67]  Nuria Oliver,et al.  Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering , 2010, RecSys '10.

[68]  Jonathan L. Herlocker,et al.  Clustering items for collaborative filtering , 1999 .

[69]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[70]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[71]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[72]  Bin Li,et al.  Cross-Domain Collaborative Filtering: A Brief Survey , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[73]  Qiang Yang,et al.  Transfer Learning in Collaborative Filtering for Sparsity Reduction , 2010, AAAI.

[74]  James Bennett,et al.  The Netflix Prize , 2007 .

[75]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[76]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[77]  Xin Liu.  Towards Context-Aware Social Recommendation via Trust Networks , 2013, WISE.

[78]  Serguei Netessine,et al.  Is Tom Cruise Threatened ? Using Netflix Prize Data to Examine the Long Tail of Electronic Commerce , 2009 .

[79]  Alexandros Karatzoglou,et al.  Learning to rank for recommender systems , 2013, RecSys.

[80]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[81]  Ryszard Janicki,et al.  Weighted Features Classification with Pairwise Comparisons, Support Vector Machines and Feature Domain Overlapping , 2013, 2013 Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[82]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[83]  Congfu Xu,et al.  Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks , 2015, Knowl. Based Syst..

[84]  Tieniu Tan,et al.  Personalized ranking with pairwise Factorization Machines , 2016, Neurocomputing.

[85]  Jon M. Kleinberg,et al.  Using mixture models for collaborative filtering , 2004, STOC '04.

[86]  Yu Zhang,et al.  A recommendation model based on collaborative filtering and factorization machines for social networks , 2013, 2013 5th IEEE International Conference on Broadband Network & Multimedia Technology.

[87]  Wu-Jun Li,et al.  TagiCoFi: tag informed collaborative filtering , 2009, RecSys '09.

[88]  Martha Larson,et al.  Bayesian Personalized Ranking with Multi-Channel User Feedback , 2016, RecSys.

[89]  Feng Liang,et al.  Exploiting ranking factorization machines for microblog retrieval , 2013, CIKM.

[90]  Lars Schmidt-Thieme,et al.  Tag-aware recommender systems by fusion of collaborative filtering algorithms , 2008, SAC '08.

[91]  Martha Larson,et al.  Towards Minimal Necessary Data: The Case for Analyzing Training Data Requirements of Recommender Algorithms , 2017 .

[92]  Ralf Krestel,et al.  Latent dirichlet allocation for tag recommendation , 2009, RecSys '09.

[93]  Peter Vojtás,et al.  Negative implicit feedback in e-commerce recommender systems , 2013, WIMS '13.

[94]  Robert M. Bell,et al.  The BellKor 2008 Solution to the Netflix Prize , 2008 .

[95]  Tong Zhang,et al.  Gradient boosting factorization machines , 2014, RecSys '14.

[96]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[97]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[98]  Martha Larson,et al.  Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering , 2011, UMAP'11.

[99]  Michele Gorgoglione,et al.  Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce , 2009, EC-Web.

[100]  Dennis DeCoste,et al.  Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations , 2006, ICML.

[101]  Liang Tang,et al.  An Empirical Study on Recommendation with Multiple Types of Feedback , 2016, KDD.

[102]  Qiang Yang,et al.  EigenRank: a ranking-oriented approach to collaborative filtering , 2008, SIGIR '08.

[103]  Shuai Wang,et al.  Contextual and Position-Aware Factorization Machines for Sentiment Classification , 2018, ArXiv.

[104]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[105]  Xavier Amatriain,et al.  Data Mining Methods for Recommender Systems , 2011, Recommender Systems Handbook.

[106]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .