Time-Dependent Models in Collaborative Filtering Based Recommender System

In recent years, time information is more and more important in collaborative filtering (CF) based recommender system because many systems have collected rating data for a long time, and time effects in user preference is stronger. In this paper, we focus on modeling time effects in CF and analyze how temporal features influence CF. There are four main types of time effects in CF: (1) time bias, the interest of whole society changes with time; (2) user bias shifting, a user may change his/her rating habit over time; (3) item bias shifting, the popularity of items changes with time; (4) user preference shifting, a user may change his/her attitude to some types of items. In this work, these four time effects are used by factorized model, which is called TimeSVD. Moreover, many other time effects are used by simple methods. Our time-dependent models are tested on Netflix data from Nov. 1999 to Dec. 2005. Experimental results show that prediction accuracy in CF can be improved significantly by using time information.

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

[2]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

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

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

[5]  Thomas Hofmann,et al.  Robust collaborative filtering , 2007, RecSys '07.

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

[7]  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.

[8]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

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

[10]  Tommi S. Jaakkola,et al.  Weighted Low-Rank Approximations , 2003, ICML.

[11]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[12]  David M. Pennock,et al.  Applying collaborative filtering techniques to movie search for better ranking and browsing , 2007, KDD '07.

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

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

[15]  Nathan Srebro,et al.  Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.

[16]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[17]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[18]  Andreas Töscher.  The BigChaos Solution to the Netflix Prize 2008 , 2008 .

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

[20]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[21]  Yehuda Koren,et al.  The BellKor solution to the Netflix Prize , 2007 .

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

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

[24]  G. Takács,et al.  On the Gravity Recommendation System , 2007 .

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