A Context-Aware and User Behavior-Based Recommender System with Regarding Social Network Analysis

Classic collaborative filtering methods suffer from lack of accuracy when it comes to todays complicated ecommerce websites. Recommender systems can be more efficient and effective by considering each user behavior model individually and in groups with other people that can be interpreted as their social relations. This paper presents a new context aware and behavior-based recommender system which utilizes social network analysis to enhance recommending results. The general idea is extracting user behavior patterns in different context based on user's past interactions with the system for further classifications. In this study we use the idea of using social network analysis with an improvement; we consider multiple relations among users and use a multi-layer social network to make our groups of preferences richer. Different social network metrics such as centralization, modularity class and so on can help us find similar groups of users in social network. We combine all the results and suggest top-K items to the user based on his/her nearest neighbor opinions. Evaluating the results shown significant improvement in accuracy of recommendations for proposed approach in contrast to previous techniques.