AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs

Xintao Ma, Liyan Dong, Yuequn Wang, Yongli Li and Minghui Sun
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Xintao Ma: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Liyan Dong: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Yuequn Wang: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Yongli Li: School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Minghui Sun: College of Computer Science and Technology, Jilin University, Changchun 130012, China

Mathematics, 2020, vol. 8, issue 12, 1-19

Abstract: With users being exposed to the growing volume of online information, the recommendation system aiming at mining the important or interesting information is becoming a modern research topic. One approach of recommendation is to integrate the graph neural network with deep learning algorithms. However, some of them are not tailored for bipartite graphs, which is a unique type of heterogeneous graph having two entity types. Others, though customized, neglect the importance of implicit relation and content information. In this paper, we propose the attentive implicit relation recommendation incorporating content information (AIRC) framework that is designed for bipartite graphs based on the GC–MC algorithm. First, through reconstructing the bipartite graphs, we obtain the implicit relation graphs. Then we analyze the content information of users and items with a CNN process, so that each user and item has its feature-tailored embeddings. Besides, we expand the GC–MC algorithms by adding a graph attention mechanism layer, which handles the implicit relation graph by highlighting important features and neighbors. Therefore, our framework takes into consideration both the implicit relation and content information. Finally, we test our framework on Movielens dataset and the results show that our framework performs better than other state-of-art recommendation algorithms.

Keywords: recommendation system; bipartite graphs; graph representation learning; matrix completion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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