A Tri-Attention Neural Network Model-BasedRecommendation. (9th October 2020)

• Record Type: Journal Article
• Authors: Wang, Nanxin; Yang, Libin; Zheng, Yu; Cai, Xiaoyan; Mei, Xin; Dai, Hang
• Other Names: Liang Huizhi Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-10-09
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/3857871
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task. The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first. Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN. After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths. Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task. Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.