Network Representation Learning Enhanced Recommendation Algorithm

Wang, Qiang, Yu, Yonghong, Gao, Haiyan, Zhang, Li, Cao, Yang, Mao, Lin, Dou, Kaiqi and Ni, Wenye (2019) Network Representation Learning Enhanced Recommendation Algorithm. IEEE Access. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2916186

Abstract

With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional recommendation algorithms. Socialnetwork- based recommendation algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of existing social-network-based recommendation algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced recommendation algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the lowdimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms.

Item Type: Article
Uncontrolled Keywords: Network representation learning, recommendation algorithm, matrix factorization, and social network
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 20 May 2019 14:20
Last Modified: 11 Oct 2019 08:52
URI: http://nrl.northumbria.ac.uk/id/eprint/39358

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