Learning social representations with deep autoencoder for recommender system - Pan, Yiteng; He, Fazhi; Yu, Haiping (World wide web (Bussum), 2020)
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.