Context-Aware Co-attention Neural Network for Service Recommendations

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Title: Context-Aware Co-attention Neural Network for Service Recommendations
Authors: Li, LeiDong, RuihaiChen, Li
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Date: 11-Apr-2019
Online since: 2021-05-25T15:40:11Z
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.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2019 IEEE
Keywords: Recommender systemsContextCo-attentionNeural network
DOI: 10.1109/ICDEW.2019.00-11
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Language: en
Status of Item: Peer reviewed
Is part of: ICDEW 2019: 2019 IEEE 35th International Conference on Data Engineering Workshops, Proceedings
Conference Details: The 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macau, China, 8-11 April 2019
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Appears in Collections:Insight Research Collection

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