DMFP: a dynamic multi-faceted fine-grained preference model for recommendation

Huizhao Wang, Guanfeng Liu, Yan Zhao, Bolong Zheng, Pengpeng Zhao, Kai Zheng

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

3 Citations (Scopus)


The time signals behind a user's historical behaviors are important for better inferring what she prefers to interact with at the next time. For the attention-based recommendation methods, relative position encoding and time intervals division are two common ways to model the time signal behind each behavior. They either only consider the relative position of each behavior in the behavior sequence, or process the continuous temporal features into discrete category features for subsequent tasks, which can hardly capture the dynamic preferences of a user. In addition, although the existing recommendation methods have considered both long-term preference and short-term preference, they ignore the fact that the long-term preference of a user may be multi-faceted, and it is difficult to learn a user's fine-grained short-term preference. In this paper, we propose a Dynamic Multi-faceted Fine-grained Preference model (DMFP), where the multi-hops attention mechanism and the feature-level attention mechanism together with a vertical convolution operation are adopted to capture users' multi-faceted long-term preference and fine-grained short-term preference, respectively. Therefore, DMFP can better support the next item recommendation. Extensive experiments on three real-world datasets illustrate that our model can improve the effectiveness of the recommendation compared with the state-of-the-art methods.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Data Mining (ICDM)
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781728146034
Publication statusPublished - 2019
EventIEEE International Conference on Data Mining (19th : 2019) - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


ConferenceIEEE International Conference on Data Mining (19th : 2019)
Abbreviated titleICDM 2019


  • Attention
  • Dynamic preference
  • Recommendation


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