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

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic

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

Proceedings of the 7th ACM conference on Recommender systems, October 2013

 

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 then two levels of relevance exist in the data. 

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