A Tri-Attention Neural Network Model-BasedRecommendation

Joint Authors

Dai, Hang
Zheng, Yu
Wang, Nanxin
Yang, Libin
Mei, Xin
Cai, Xiaoyan

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-09

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Philosophy

Abstract EN

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.

American Psychological Association (APA)

Wang, Nanxin& Yang, Libin& Zheng, Yu& Cai, Xiaoyan& Mei, Xin& Dai, Hang. 2020. A Tri-Attention Neural Network Model-BasedRecommendation. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1141735

Modern Language Association (MLA)

Wang, Nanxin…[et al.]. A Tri-Attention Neural Network Model-BasedRecommendation. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1141735

American Medical Association (AMA)

Wang, Nanxin& Yang, Libin& Zheng, Yu& Cai, Xiaoyan& Mei, Xin& Dai, Hang. A Tri-Attention Neural Network Model-BasedRecommendation. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1141735

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1141735