Robust Approach to Generating Location- Sensitive Recommendations in Ad-hoc Social Network Environments

Social recommendation is popular and successful among 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 some 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 considered in their recommendation mechanisms. With the rapid development of the service of locationbased social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. This project evaluates and compares the proposed approach with the existing rating prediction and item recommendation algorithms. The results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user’s preference and location.