Efficient and Anonymous Web-Usage Mining for Web Personalization
INFORMS Journal on Computing
Proceedings of the 16th international conference on World Wide Web
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
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Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
On Monitoring the top-k Unsafe Places
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Inferring intra-organizational collaboration from cosine similarity distributions in text documents
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ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
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IEEE Transactions on Knowledge and Data Engineering
Towards integrating real-world spatiotemporal data with social networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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As the popularity of social networks is continuously growing, collected data about online social activities is becoming an important asset enabling many applications such as target advertising, sale promotions, and marketing campaigns. Although most social interactions are recorded through online activities, we believe that social experiences taking place offline in the real physical world are equally if not more important. This paper introduces a geo-social model that derives social activities from the history of people's movements in the real world, i.e., who has been where and when. In particular, from spatiotemporal histories, we infer real-world co-occurrences - being there at the same time - and then use co-occurrences to quantify social distances between any two persons. We show that straightforward approaches either do not scale or may overestimate the strength of social connections by giving too much weight to coincidences. The experiments show that our model well captures social relationships between people, even on partially available data.