CAESAR: context-aware explanation based on supervised attention for service recommendations

Lei Li*, Li CHEN, Ruihai Dong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.

Original languageEnglish
JournalJournal of Intelligent Information Systems
DOIs
Publication statusAccepted/In press - 2020

Scopus Subject Areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Context-aware recommender systems
  • Explainable recommendation
  • Multi-task learning
  • Neural network

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