A unified latent factor model for effective category-aware recommendation

Zhu Sun, Guibing Guo, Jie Zhang, Chi Xu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

Our data analysis on real-world datasets shows that user preferences are intimately related with item categories, implying the nonnegligible of category information for effective recommendation. Thus, in this paper, step by step we propose a unified item-category latent factor model by considering user-category, item-category and category-category interactions. Our approach can be applied to both the situations where an item belongs to either a single category (one-To-one) or multiple categories (one-To-many). Finally, empirical studies on the real-world datasets demonstrate the superiority of our approach in comparison with other counterparts.

Original languageEnglish
Title of host publicationUMAP 2017
Subtitle of host publicationProceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages389-390
Number of pages2
ISBN (Electronic)9781450346351
DOIs
Publication statusPublished - 9 Jul 2017
Externally publishedYes
Event25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017 - Bratislava, Slovakia
Duration: 9 Jul 201712 Jul 2017

Conference

Conference25th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2017
CountrySlovakia
CityBratislava
Period9/07/1712/07/17

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