- Title
- Deep matrix factorization for trust-aware recommendation in social networks
- Creator
- Wan, Liangtian; Xia, Feng; Kong, Xiangjie; Hsu, Ching-Hsien; Huang, Runhe; Ma, Jianhua
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176403
- Identifier
- vital:15130
- Identifier
-
https://doi.org/10.1109/TNSE.2020.3044035
- Identifier
- ISBN:2327-4697 (ISSN)
- Abstract
- Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- IEEE Transactions on Network Science and Engineering Vol. 8, no. 1 (2021), p. 511-528
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright 2021 IEEE - All rights reserved
- Rights
- Open Access
- Subject
- 0906 Electrical and Electronic Engineering; 1608 Sociology; Autoencoder; Deep learning; Matrix factorization; Social networks; Trust relationship
- Full Text
- Reviewed
- Funder
- Manuscript received January 5, 2019; revised December 21, 2019 and August 16, 2020; accepted December 2, 2020. Date of publication December 11, 2020; date of current version March 17, 2021. This work is partially supported by National Natural Science Foundation of China (61 801 076, 61 872 054, 62 072 409), Fundamental Research Funds for the Central Universities (DUT20JC29), Zhejiang Provincial Natural Science Foundation (LR21F020003), and Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-B2020001). Recommended for acceptance by Dr. Jie Li. (Corresponding author: Feng Xia.) Liangtian Wan is with the Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116 620, China (e-mail: wan.liangtian.2015@ieee.org).
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