Deep learning has yielded success in many research fields. In the last few years, deep learning techniques have been applied in recommender systems to solve cold start and data sparsity problems. However, only a few attempts have been made in social-based recommender systems. In this study, we address this issue and propose a novel recommendation model called Sentiment based Social Neural Hybrid Filtering (S-SNHF). This model combines collaborative and content-based filtering with social information using a deep neural architecture based on Generalized Matrix Factorization (GMF) and Hybrid Multilayer Perceptron (HybMLP). Furthermore, for achieving higher recommendation reliability, the hybrid sentiment analysis model is integrated to analyse users’ opinions and infer their preferences. The results of the empirical study performed with three popular datasets show the contribution of both, social information and sentiment analysis on the recommendation performance and that our approach achieves significantly better recommendation accuracy, compared with state-of-the-art recommendation methods.

深度学习在许多研究领域都取得了成功。在过去的几年中，深度学习技术已应用于推荐系统，以解决冷启动和数据稀疏问题。然而，在基于社交的推荐系统中只进行了一些尝试。在这项研究中，我们解决了这个问题并提出了一种新的推荐模型，称为基于情感的社会神经混合过滤 (S-SNHF)。该模型使用基于广义矩阵分解 (GMF) 和混合多层感知器 (HybMLP) 的深度神经架构，将协作和基于内容的过滤与社交信息相结合。此外，为了获得更高的推荐可靠性，集成了混合情感分析模型来分析用户的意见并推断他们的偏好。