Abstract: Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users 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 have investigated 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 location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. The selection of the best service from the ones available is a conundrum to predict as different users will follow different selection techniques. Selection of the web service is directly related to the quality of service (QoS) provided. This paper proposes a learning-to-rank algorithm to comprehend the decision strategy of users in choosing the specific web service. 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. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of userís preference and location.
Keywords: Spatial Social Union (SSU), Quality of Service (QoS), Learning-To-Rank, Decision Strategy.