Improving the Performance of User-Based Collaborative Filtering by Mining Latent Attributes of Neighborhood

In the area of recommender systems, user-based collaborative filtering algorithm has been extensively studied and discussed. In the traditional approach of this method, a target user's preference for an item is predicted by the integrated preference of the user's neighbors for the item, ignoring the structure of these neighbors. That is, these neighbors form two distinct groups: some neighbors may like the target item or give high rating, on the other hand, some neighbors may dislike the target item or give low rating. The structure of the two groups may influence user's choice. As an extension of user-based collaborative filtering, this paper focuses on the analysis of such structure by mining latent attributes of users' neighborhood, and corresponding correlations with users' preference by several popular data mining techniques. Mining latent attributes and experiment evaluation was conducted on Movie Lens data set. The experimental results reveal that the proposed method can improve the performance of pure user-based collaborative filtering algorithm.

[1]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[2]  W. Behrendt,et al.  A Trail Based Internet-Domain Recommender System using Artificial Neural Networks , 2002 .

[3]  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.

[4]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[5]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[6]  Siegfried Reich,et al.  Recommending Internet-Domains Using Trails and Neural Networks , 2002, AH.

[7]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[8]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[9]  Seong Joon Yoo,et al.  SVM and Collaborative Filtering-Based Prediction of User Preference for Digital Fashion Recommendation Systems , 2007, IEICE Trans. Inf. Syst..

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

[11]  Weiqing Wang,et al.  User-based collaborative filtering on cross domain by tag transfer learning , 2012, CDKD '12.

[12]  Mehdi Shajari,et al.  Who are the most influential users in a recommender system? , 2011, ICEC '11.

[13]  Michael J. Pazzani,et al.  Collaborative Filtering with the Simple Bayesian Classifier , 2000, PRICAI.

[14]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[15]  Qiang Yang,et al.  Scalable collaborative filtering using cluster-based smoothing , 2005, SIGIR '05.

[16]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

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