S-SNHF: sentiment based social neural hybrid filtering

Abstract

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.