Context-aware co-attention neural network for service recommendations

Lei Li, Ruihai Dong, Li CHEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Context-aware recommender systems are able to produce more accurate recommendations by harnessing contextual information, such as consuming time and location. Further, user reviews as an important information resource, providing valuable information about users' preferences, items' aspects, and implicit contextual features, could be used to enhance the embeddings of users, items, and contexts. However, few works attempt to incorporate these two types of information, i.e., contexts and reviews, into their models. Recent state-of-the-art context-aware methods only characterize relations between two types of entities among users, items and contexts, which may be insufficient, as the final prediction is closely related to all the three types of entities. In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of matching between users' contextual preferences and items' context-aware aspects via co-attention mechanism. To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual review. Experimental results, on three datasets composed of millions of review records crawled from TripAdvisor, demonstrate that our CCANN significantly outperforms state-of-the-art recommendation methods, and Entity2Vec can further boost the model's performance.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages201-208
Number of pages8
ISBN (Electronic)9781728108902
DOIs
Publication statusPublished - Apr 2019
Event35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019 - Macau, China
Duration: 8 Apr 201912 Apr 2019

Publication series

NameProceedings - 2019 IEEE 35th International Conference on Data Engineering Workshops, ICDEW 2019

Conference

Conference35th IEEE International Conference on Data Engineering Workshops, ICDEW 2019
Country/TerritoryChina
CityMacau
Period8/04/1912/04/19

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Artificial Intelligence

User-Defined Keywords

  • Co-attention
  • Context
  • Neural network
  • Recommender systems

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