Multi-facet user preference learning for fine-grained item recommendation

Xin Zhou, Guibing Guo, Zhu Sun, Yuan Liu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Existing recommendation methods mainly learn user preference from historical user-item interaction data, while ignoring the extent of interactions, i.e., diverse user experience and user intention. To be more specific, for new users with little or no experience, they may turn to popular items preferred by majority users. In terms of those medium-level users who already have interacted with some items, they may require and expect items to meet their personal preferences. As pro-active users are likely to leave rich behavior (action) feedback (e.g., view, like) on the items they interacted with, the system will have good chance to better interpret users’ intention, and thus generate more accurate and elaborate recommendations to hit their preferences. In this paper, we propose a generic Multi-facet User Preference Learning (MUPL) framework for fine-grained item recommendation. By considering diverse user experience and intention, MUPL captures user preference in the level of group-, individual- and action-facet. Besides, the importance of user preference in different facets can be automatically learnt by MUPL. Extensive experiments on two real-world datasets (Xing, Sobazaar) demonstrate the superiority of our proposed approach over other state-of-the-art methods.
Original languageEnglish
Pages (from-to)258-268
Number of pages11
Early online date26 Dec 2019
Publication statusPublished - 14 Apr 2020
Externally publishedYes


  • Behavior feedback
  • Item recommendation
  • Multi-facet user preference
  • Recommender system


Dive into the research topics of 'Multi-facet user preference learning for fine-grained item recommendation'. Together they form a unique fingerprint.

Cite this