Learning hierarchical category influence on both users and items for effective recommendation

Zhu Sun, Guibing Guo, Jie Zhang

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

2 Citations (Scopus)

Abstract

Item category has proven to be useful additional information to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a at form, in many real applications, item category is often organized in a richer knowledge structure - category hierarchy, to reect the inherent correlations among different categories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifically, a user can be inuenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the inuence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms.

Original languageEnglish
Title of host publication32nd Annual ACM Symposium on Applied Computing, SAC 2017
PublisherAssociation for Computing Machinery
Pages1679-1684
Number of pages6
ISBN (Electronic)9781450344869
DOIs
Publication statusPublished - 3 Apr 2017
Externally publishedYes
Event32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco
Duration: 4 Apr 20176 Apr 2017

Conference

Conference32nd Annual ACM Symposium on Applied Computing, SAC 2017
CountryMorocco
CityMarrakesh
Period4/04/176/04/17

Keywords

  • Category hierarchy
  • Item category
  • Latent factor model
  • Recommender systems

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