Context-Aware Co-attention Neural Network for Service Recommendations

Lei Li, Ruihai Dong, Li Chen
2019 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)  
Publisher IEEE Link to online version 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 source, 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
more » ... 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 coattention 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 En-tity2Vec can further boost the model's performance.
doi:10.1109/icdew.2019.00-11 dblp:conf/icde/LiDC19 fatcat:6iuq5zni7bc6pltewmcdp6aab4