Title

Dynamic Graph Attention-Aware Networks for Session-Based Recommendation

Author First name, Last name, Institution

Ahed Abugabah
Xiaochun Cheng
Jianfeng Wang

Document Type

Conference Proceeding

Source of Publication

2020 International Joint Conference on Neural Networks (IJCNN)

Publication Date

1-1-2020

Abstract

Graph convolutional neural networks have attracted increasing attention in recommendation system fields because of their ability to represent the interactive relations between users and items. At present, there are many session-based methods based on graph neural networks. For example, SR-GNN establishes a user’s session graph based on the user’s sequential behavior to predict the user’s next click. Although these session-based recommendation methods modeling the user’s interaction with items as a graph, these methods have achieved good performance in improving the accuracy of the recommendation. However, most existing models ignore the items’ relationship among sessions. To efficiently learn the deep connections between graph-structured items, we devised a dynamic attention-aware network (DYAGNN) to model the user’s potential behavior sequence for the recommendation. Extensive experiments have been conducted on two real-world datasets, the experimental results demonstrate that our method achieves good results in capturing user attention perception.

ISBN

978-1-7281-6926-2

Publisher

IEEE

Volume

00

Last Page

7

Disciplines

Computer Sciences | Education | Psychology

Keywords

Predictive models, Task analysis, Aggregates, Data models, Logic gates

Scopus ID

85104096182

Indexed in Scopus

yes

Open Access

no

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