Collaborative Filtering for Implicit Feedback Datasets

A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.

[1]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[2]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[3]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[4]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[5]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[6]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

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

[8]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[9]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

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

[11]  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.

[12]  Padhraic Smyth,et al.  KDD Cup and workshop 2007 , 2007, SKDD.

[13]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[14]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

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

[16]  Hsinchun Chen,et al.  A comparison of collaborative-filtering algorithms for ecommerce , 2007 .

[17]  Hsinchun Chen,et al.  A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce , 2007, IEEE Intelligent Systems.

[18]  Yehuda Koren,et al.  Modeling relationships at multiple scales to improve accuracy of large recommender systems , 2007, KDD '07.

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

[20]  Arkadiusz Paterek,et al.  Improving regularized singular value decomposition for collaborative filtering , 2007 .

[21]  Domonkos Tikk,et al.  Major components of the gravity recommendation system , 2007, SKDD.