Learning social representations with deep autoencoder for recommender system

With the development of online social media, it attracts increasingly attentions to utilize social information for recommender systems. Based on the intuition that users are influenced by their social friends, these methods are capable of addressing the data sparse problem and improving the performa...

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Bibliographic Details
Published in:World wide web (Bussum) Vol. 23; no. 4; pp. 2259 - 2279
Main Authors: Pan, Yiteng, He, Fazhi, Yu, Haiping
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2020
Springer Nature B.V
Subjects:
Computer Science
Database Management
Digital media
Factorization
Information Systems Applications (incl.Internet)
Neural networks
Noise reduction
Operating Systems
Performance enhancement
Recommender systems
Representations
Social networks
Deep learning
Denoising autoencoder
Matrix factorization
Social recommendation
ISSN:1386-145X, 1573-1413
Online Access:Get full text
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Summary:With the development of online social media, it attracts increasingly attentions to utilize social information for recommender systems. Based on the intuition that users are influenced by their social friends, these methods are capable of addressing the data sparse problem and improving the performance of recommender systems. However, these methods model the influences between each pair of users independently and ignore the interactions among these social influences, i.e., high-level signal of social information. In this paper, we propose a deep autoencoder model to learn social representations for recommender system. This approach aims to learn low- and high- level features from social information based on muti-layers neural networks and matrix factorization technique. Especially, we develop an improved deep autoencoder model, named Sparse Stacked Denoising Autoencoder (SSDAE), to address the data sparse and imbalance problems for social networks. Moreover, we incorporate these deep representations and matrix factorization model into a uniform framework for recommender system. Our experiments in Epinions and Ciao datasets demonstrate that our method can significantly improve the performance of recommender system, especially for sparse users.
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ISSN:1386-145X
1573-1413
DOI:10.1007/s11280-020-00793-z