Investigation of Various Matrix Factorization Methods for Large Recommender Systems

Matrix factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-positive MF approach that approximates the features by using positive values for either users or items. We describe a momentum-based MF approach. A transductive version of MF is also introduced, which uses information from test instances (namely the ratings users have given for certain items) to improve prediction accuracy. We describe an incremental variant of MF that efficiently handles new users/ratings, which is crucial in a real-life recommender system. A hybrid MF--neighbor-based method is also discussed that further improves the performance of MF.The proposed methods are evaluated on the Netflix Prize dataset, and we show that they can achieve very favorable Quiz RMSE (best single method: 0.8904, combination: 0.8841) and running time.

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

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

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

[4]  Tommi S. Jaakkola,et al.  Maximum-Margin Matrix Factorization , 2004, NIPS.

[5]  András A. Benczúr,et al.  Methods for large scale SVD with missing values , 2007 .

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

[7]  Padhraic Smyth,et al.  KDD Cup and Workshop 2007 , 2007, KDD '07.

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

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

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

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

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

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

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

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

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

[17]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.