Mining top-k granular association rules for recommendation

Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measures the strength of the association. With the confidence measure, rules can be ranked according to their strength. Then we propose algorithms for training the recommender and suggesting items to each user. Experimental are undertaken on a publicly available data set MovieLens. Results indicate that the appropriate setting of granule can avoid over-fitting and at the same time, help obtaining high recommending accuracy.

[1]  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.

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

[3]  Yiyu Yao,et al.  A Granular Computing Paradigm for Concept Learning , 2013 .

[4]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[5]  Qinghua Hu,et al.  Granular association rules on two universes with four measures , 2012, ArXiv.

[6]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[7]  Loriene Roy,et al.  Content-based book recommending using learning for text categorization , 1999, DL '00.

[8]  Gregoris Mentzas,et al.  Information market based recommender systems fusion , 2011, HetRec '11.

[9]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[10]  William Zhu,et al.  Granular association rules for multi-valued data , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[11]  Witold Pedrycz,et al.  Granular Computing: Perspectives and Challenges , 2013, IEEE Transactions on Cybernetics.

[12]  Qinghua Hu,et al.  Granular association rules with four subtypes , 2012, 2012 IEEE International Conference on Granular Computing.

[13]  Andrzej Skowron,et al.  Approximation of Relations , 1993, RSKD.

[14]  Cherié L. Weible,et al.  The Internet Movie Database , 2001 .

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

[16]  Susan T. Dumais,et al.  Personalized information delivery: an analysis of information filtering methods , 1992, CACM.

[17]  Robert A. Legenstein,et al.  Combining predictions for accurate recommender systems , 2010, KDD.

[18]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[19]  Fabio Airoldi,et al.  Hybrid algorithms for recommending new items , 2011, HetRec '11.