Abstract

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.

Original languageEnglish (US)
JournalIEEE Transactions on Knowledge and Data Engineering
DOIs
StateAccepted/In press - Jan 3 2018

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Recommender systems
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Keywords

  • deep nonnegative factorization
  • explicit hierarchical structures
  • implicit hierarchical structures
  • Recommender system

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Exploring Hierarchical Structures for Recommender Systems. / Wang, Suhang; Tang, Jiliang; Wang, Yilin; Liu, Huan.

In: IEEE Transactions on Knowledge and Data Engineering, 03.01.2018.

Research output: Contribution to journalArticle

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