Research Area:  Big Data
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 users 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 users 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.
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Author(s) Name:  Guoshuai Zhao,Xueming Qian and Chen Kang
Journal name:  IEEE Transactions on Big Data
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Publisher name:  IEEE
DOI:  10.1109/TBDATA.2016.2552541
Volume Information:  March 2017, pp. 67-78, vol. 3
Paper Link:   https://www.computer.org/csdl/journal/bd/2017/01/07458855/13rRUyft7wE