Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Friend recommendation according to appearances on photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Find you wherever you are: geographic location and environment context-based pedestrian detection
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Unified entity search in social media community
Proceedings of the 22nd international conference on World Wide Web
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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.