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.

中文翻译：

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使用深度自动编码器为推荐系统学习社交表示

随着在线社交媒体的发展，将社交信息用于推荐系统越来越受到关注。基于用户受其社交朋友影响的直觉，这些方法能够解决数据稀疏问题并提高推荐系统的性能。然而，这些方法独立地模拟了每对用户之间的影响，而忽略了这些社会影响之间的相互作用，即社会信息的高层信号。在本文中，我们提出了一种深度自动编码器模型，以学习推荐系统的社交表示。该方法旨在基于多层神经网络和矩阵分解技术从社交信息中学习低级和高级功能。特别是，我们开发了一种改进的深度自动编码器模型，名为稀疏堆叠式降噪自动编码器（SSDAE），用于解决社交网络的数据稀疏和不平衡问题。此外，我们将这些深层表示和矩阵分解模型纳入了推荐系统的统一框架中。我们在Epinions和Ciao数据集中的实验表明，我们的方法可以显着改善推荐系统的性能，尤其是对于稀疏用户。