Conference Proceedings

A joint optimization approach for personalized recommendation diversification

Xiaojie Wang, Jianzhong Qi, Ramamohanarao Kotagiri, Yu Sun, Bo Li, Rui Zhang, Dinh Phung (ed.), Vincent S Tseng (ed.), Geoffrey I Webb (ed.), Bao Ho (ed.), Mohadeseh Ganji (ed.), Lida Rashidi (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer | Published : 2018


© Springer International Publishing AG, part of Springer Nature 2018. In recommendation systems, items of interest are often classified into categories such as genres of movies. Existing research has shown that diversified recommendations can improve real user experience. However, most existing methods do not consider the fact that users’ levels of interest (i.e., user preferences) in different categories usually vary, and such user preferences are not reflected in the diversified recommendations. We propose an algorithm that considers user preferences for different categories when recommending diversified results, and refer to this problem as personalized recommendation diversification. In ..

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