FuzzAttention on Session-based Recommender System

Chi Shiang Wang, Jung Hsien Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


mender system is studied widely and has been implemented successfully into businesses and daily lives. Its primary purpose is to analyze user behaviors to predict the next item of interest. To enrich user information, a session-based recommender system considers the user's historical records in the session. This information includes the user's general and current interests as additional information to improve the performance of the model such that the next item can be recommended easily. However, the recommender system not only considers the prediction performance but also regards the interpretability as a major target. For enhancing the interpretability and performance, we propose a novel mechanism, i.e., FuzzAttention, based on the attention mechanism that is applied widely to deep-learning models. In FuzzAttention, we utilize a fuzzy neural network to build a fuzzy inference system; therefore, we adopt joint learning to learn the parameters in the session-based recommendation and the fuzzy neural network jointly. In the experiments, we used two types of session-based recommender systems and conducted them on two datasets including the session-based information to compare the model performance based on the traditional attention mechanism and FuzzAttention. The results indicate that our proposed mechanism can improve the performance to predict the next item and the model's interpretability.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538617281
Publication statusPublished - 2019 Jun
Event2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019 - New Orleans, United States
Duration: 2019 Jun 232019 Jun 26

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


Conference2019 IEEE International Conference on Fuzzy Systems, FUZZ 2019
Country/TerritoryUnited States
CityNew Orleans

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


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