Effective recommendation with category hierarchy

Zhu Sun, Guibing Guo, Jie Zhang

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

2 Citations (Scopus)

Abstract

Although at item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be inuenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy coinuence a user and an item. Empirical results show the superiority of our approach against other counterparts.

Original languageEnglish
Title of host publicationProceedings of the 2016 Conference on User Modeling Adaptation and Personalization
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages299-300
Number of pages2
ISBN (Electronic)9781450343701
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada
Duration: 13 Jul 201617 Jul 2016

Conference

Conference24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
CountryCanada
CityHalifax
Period13/07/1617/07/16

Keywords

  • Category hierarchy
  • Matrix factorization
  • Recommendation

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