Title: A collaborative filtering recommendation algorithm based on DeepWalk and self-attention

Authors: Jiaming Guo; Hong Wen; Weihong Huang; Ce Yang

Addresses: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Services Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Services Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Services Computing and Novel Software Technology, Xiangtan 411201, China ' School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Services Computing and Novel Software Technology, Xiangtan 411201, China

Abstract: Graph embedding is one of the vital technologies in solving the problem of information overload in recommendation systems. It can simplify the vector representations of items and accelerates the calculation process. Unfortunately, the recommendation system using graph embedding technology does not consider the deep relationships between items when it learns embedding vectors. In order to solve this problem, we propose a collaborative filtering recommendation algorithm based on DeepWalk and self-attention in this paper. This algorithm can enhance the accuracy in measuring the similarity between items and obtain more accurate embedding vectors. Chronological order and mutual information are used to construct a weighted directed relationship graph. Self-attention and DeepWalk are utilised to generate embedding vectors. Then item-based collaborative filtering is utilised to obtain recommended lists. The result of the relative experiments and evaluations on three public datasets shows that our algorithm is better than the existing ones.

Keywords: DeepWalk; self-attention; mutual information; collaborative filtering; recommendation algorithm.

DOI: 10.1504/IJCSE.2023.131503

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.296 - 304

Received: 15 Feb 2022
Accepted: 06 Mar 2022
Published online: 15 Jun 2023 *

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