Issue No. 01 - March (2017 vol. 3)
Guoshuai Zhao , SMILES LAB of Xi’an Jiaotong University, Xi’an, China
Xueming Qian , Ministry of Education Key Laboratory for Intelligent Networks and Network Security
Chen Kang , SMILES LAB of Xi’an Jiaotong University, Xi’an, China
Recently, advances in intelligent mobile device and positioning techniques have fundamentally enhanced social networks, which allows users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the connection between user's physical behaviors and virtual social networks structured by the smart phone or web services. We refer to these social networks involving geographical information as location-based social networks (LBSNs). Such information brings opportunities and challenges for recommender systems to solve the cold start, sparsity problem of datasets and rating prediction. In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out rating prediction. We mine: 1) the relevance between user's ratings and user-item geographical location distances, called as user-item geographical connection, 2) the relevance between users’ rating differences and user-user geographical location distances, called as user-user geographical connection. It is discovered that humans’ rating behaviors are affected by geographical location significantly. Moreover, three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity, are fused into a unified rating prediction model. We conduct a series of experiments on a real social rating network dataset Yelp. Experimental results demonstrate that the proposed approach outperforms existing models.
Social network services, Recommender systems, Big data, Smart phones, Predictive models, Mobile communication
G. Zhao, X. Qian and C. Kang, "Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations," in IEEE Transactions on Big Data, vol. 3, no. 1, pp. 67-78, 2017.