A novel social search model based on clustering friends in LBSNs

Abstract

With the development of online social networks (OSNs), OSNs have become an indispensable part in people’s life. People tend to search information through OSNs rather than traditional search engines. Especially with the appearance of location-based social networks (LBSNs), social search in LBSNs is increasingly important in the burgeoning mobile trend. This paper proposes a novel social search model, harnesses users’ social relationship and location features provided by LBSNs to design a ranking algorithm that takes three kinds of ranking scores into account comprehensively: Social Score (scores based on social influence), Searching Score (scores based on professional relevance) and Spatial Score (scores based on distance), finally produces high-quality searching results. Once receiving users’ query, the social search engine aims to return a list of ranking POIs (points of interests) that satisfies users. The dataset is extracted from Foursquare, a real-world LBSN. The experiment results show that the ranking algorithm can benefit the social search model in LBSNs evidently.