Iterative collaborative filtering for recommender systems with sparse data

Collaborative filtering (CF) is one of the most successful techniques in recommender systems. By utilizing co-rated items of pairwise users for similarity measurements, traditional CF uses a weighted summation to predict unknown ratings based on the available ones. However, in practice, the rating matrix is too sparse to find sufficiently many co-rated items, thus leading to inaccurate predictions. To address the case of sparse data, we propose an iterative CF that updates the similarity and rating matrix. The improved CF incrementally selects reliable subsets of missing ratings based on an adaptive parameter and therefore produces a more credible prediction based on similarity. Experimental results on the MovieLens dataset show that our algorithm significantly outperforms traditional CF, Default Voting, and SVD when the data is 1% sparse. The results also show that in the dense data case our algorithm performs as well as state of art methods.

[1]  Gerard Salton,et al.  Research and Development in Information Retrieval , 1982, Lecture Notes in Computer Science.

[2]  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.

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

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

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

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

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

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

[9]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[10]  Bong-Jin Yum,et al.  Collaborative filtering based on iterative principal component analysis , 2005, Expert Syst. Appl..

[11]  Jun Wang,et al.  Unifying user-based and item-based collaborative filtering approaches by similarity fusion , 2006, SIGIR.

[12]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[13]  Naohiro Ishii,et al.  Memory-Based Weighted-Majority Prediction for Recommender Systems , 1999, SIGIR 1999.

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

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

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

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