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.

中文翻译：

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社交网络中用于信任感知推荐的深度矩阵分解

近年来，在线社交网络中出现了显着的信息过载，并且针对推荐系统的基于社交网络的方法已得到广泛研究。用户之间社交网络中的信任信息是提高推荐性能的重要因素。许多成功的推荐任务被视为矩阵分解问题。但是，基于矩阵分解的方法的预测性能在很大程度上取决于用户和项目的矩阵初始化。为了解决这一挑战，我们基于深度学习开发了一种新颖的信任感知方法，以减轻初始化依赖性。首先，我们提出了两种深度矩阵分解（DMF）技术，即线性DMF和非线性DMF，以从用户项评级矩阵中提取特征，以提高初始化精度。根据偏好相似度和用户对项目的推导，将信任关系集成到DMF模型中。其次，我们利用深度边缘化降噪自动编码器（Deep-MDAE）从信任关系矩阵中提取隐藏层中的潜在表示，以近似从用户项评级矩阵分解后的用户因子矩阵。社区正则化被集成在联合优化功能中，以考虑邻居的影响。DMF的结果被用于初始化Deep-MDAE的更新变量，以进一步提高推荐性能。最后，我们验证了所提出的方法优于最新的推荐基准，尤其是对于冷启动用户而言。