xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance

Authors:
Yue Shi;Alexandros Karatzoglou;Linas Baltrunas;Martha Larson;Alan Hanjalic
Affiliations:
Delft University of Technology, Delft, Netherlands;Telefonica Research, Barcelona, Spain;Telefonica Research, Barcelona, Spain;Delft University of Technology, Delft, Netherlands;Delft University of Technology, Delft, Netherlands
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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Abstract

Extended Collaborative Less-is-More Filtering xCLiMF is a learning to rank model for collaborative filtering that is specifically designed for use with data where information on the level of relevance of the recommendations exists, e.g. through ratings. xCLiMF can be seen as a generalization of the Collaborative Less-is-More Filtering (CLiMF) method that was proposed for top-N recommendations using binary relevance (implicit feedback) data. The key contribution of the xCLiMF algorithm is that it builds a recommendation model by optimizing Expected Reciprocal Rank, an evaluation metric that generalizes reciprocal rank in order to incorporate user feedback with multiple levels of relevance. Experimental results on real-world datasets show the effectiveness of xCLiMF, and also demonstrate its advantage over CLiMF when more than two levels of relevance exist in the data.