With the popularity of social media, web users tend to spend more time than before for sharing their experience and interest in online photo-sharing sites. The wide variety of sharing behaviors generate different metadata which pose new opportunities for the discovery of communities. We propose a new approach, named context-based friend suggestion, to leverage the diverse form of contextual cues for more effective friend suggestion in the social media community. Different from existing approaches, we consider both visual and geographical cues, and develop two user-based similarity measurements, i.e., visual similarity and geo similarity for characterizing user relationship. The problem of friend suggestion is casted as a contextual graph modeling problem, where users are nodes and the edges between them are weighted by geo similarity. Meanwhile, the graph is initialized in a way that users with higher visual similarity to a given query have better chance to be recommended. Experimental results on a dataset of 13,876 users and ~1.5 million of their shared photos demonstrated that the proposed approach is consistent with human perception and outperforms other works.
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