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.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (61272531, 61202449, 61272054, 61370207, 61370208, 61300024, 61320106007 and 61472081), China high technology 863 program (2013AA013503), Jiangsu Technology Planning Program (SBY2014021039-10), Jiangsu Provincial Key Laboratory of Network and Information Security under Grant No. BM2003201 and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant No. 93k-9.
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Sun, Y., Cao, J., Zhou, T., Xu, S. (2017). A Novel Social Search Model Based on Clustering Friends in LBSNs. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_68
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