A joint optimization approach for personalized recommendation diversification

Xiaojie Wang, Jianzhong Qi, Kotagiri Ramamohanarao, Yu Sun, Bo Li, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 the proposed algorithm, a model that captures user preferences for different categories is optimized jointly toward both relevance and diversity. To provide the proposed algorithm with informative training labels and effectively evaluate recommendation diversity, we also propose a new personalized diversity measure. The proposed measure overcomes limitations of existing measures in evaluating recommendation diversity: existing measures either cannot effectively handle user preferences for different categories, or cannot evaluate both relevance and diversity at the same time. Experiments using two real-world datasets confirm the superiority of the proposed algorithm, and show the effectiveness of the proposed measure in capturing user preferences.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
PublisherSpringer-Verlag Berlin Heidelberg
Pages597-609
Number of pages13
ISBN (Print)9783319930398
DOIs
StatePublished - 2018
Event22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia
Duration: Jun 3 2018Jun 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
CountryAustralia
CityMelbourne
Period6/3/186/6/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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