Top-N Recommendation via Joint Cross-Domain User Clustering and Similarity Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)

Abstract

A cross-domain recommendation algorithm exploits user preferences from multiple domains to solve the data sparsity and cold-start problems, in order to improve the recommendation accuracy. In this study, we propose an efficient Joint cross-domain user Clustering and Similarity Learning recommendation algorithm, namely JCSL. We formulate a joint objective function to perform adaptive user clustering in each domain, when calculating the user-based and cluster-based similarities across the multiple domains. In addition, the objective function uses an \(L_{2,1}\) regularization term, to consider the sparsity that occurs in the user-based and cluster-based similarities between multiple domains. The joint problem is solved via an efficient alternating optimization algorithm, which adapts the clustering solutions in each iteration so as to jointly compute the user-based and cluster-based similarities. Our experiments on ten cross-domain recommendation tasks show that JCSL outperforms other state-of-the-art cross-domain strategies.

Keywords

Collaborative filtering Cross-domain recommendation Alternating optimization 

Notes

Acknowledgments

We would like to thank Nima Mirbakhsh and Charles Ling for providing us with the evaluation data of the ten cross-domain tasks.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Faculty of InformaticsUniversità della Svizzera Italiana (USI)LuganoSwitzerland

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