Social Neural Hybrid Recommendation with Deep Representation Learning

Abstract

Deep learning techniques are proved to be very effective in various domains, such as computer vision, pattern recognition, and natural language processing. With the rapid development of this technology, more and more deep models have been used in the recommendation system. However, only few attempts have been made in social-based recommender systems. This paper focuses on this issue and explores the use of deep neural networks for learning the interaction function from data. A novel recommendation model called SNHF (Social Neural Hybrid Filtering) is proposed. It combines collaborative and content-based filtering with social information in an architecture based on both models: (1) Generalized Matrix Factorization (GMF); and (2) Hybrid Multilayer Perceptron (HybMLP). Extensive experiments on two real-world datasets show that SNHF significantly outperforms state-of-the-art baselines and related work, especially in the cold start situation.