Analysis of Social Recommendation Model using Data Mining Techniques

Abstract: Social recommendation is popular and successful between various urban sustainable applications like products recommendation, online sharing and shopping services. Users make use of these applications to form several implicit social networks through their daily social interactions. The users in such social networks can rate several interesting items and give comments. The majority of the existing studies investigate the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not consider in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes Index Base Spatial Social union (IB-SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The IB-SSU-aware location-sensitive recommendation algorithm is then devised. This paper evaluates and compares the proposed approach with the existing rating prediction and item recommendation algorithms. The results show that the proposed IB-SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user�s preference and location.

Keywords: Rating prediction, recommendation, IB-SSU.