Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FriendSensing: recommending friends using mobile phones
Proceedings of the third ACM conference on Recommender systems
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Modeling and representing events in multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Event detection and scene attraction by very simple contextual cues
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Event-based classification of social media streams
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Social event detection using multimodal clustering and integrating supervisory signals
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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This paper is based upon an approach for automatic detection of personal events in on-line personal photo collections and proposes a powerful exploitation of these events: We compose social events out of personal events and then automatically reveal interpersonal ties. Trying to tame the stream of big data in social networks we solely rely on image meta-data of time and space. We validate our assumptions in the wild using 1.8 million public images of more than 4100 users. The proposed approach has three main steps: (i) personal event detection using individual, unsorted photo collections, in which we make use of the spatio-temporal context embedded in digital photos to detect event boundaries within the collection; (ii) social event detection for which we use a tailored similarity measurement between personal events of different users; and (iii) an analysis of event co-participation to propagate social connections. Experiments validate that the fully automated approach is able to accurately detect 78.76% of social events and reconstruct the interpersonal ties of a user with a verified true positive rate of 45%. This rate is probably much higher: Since most interpersonal ties are undefined in the universe of social networks, our experimental ground-truth of course remains fragmentary.