What's your next move: User activity prediction in location-based social networks
Refereed conference paper presented and published in conference proceedings

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AbstractLocation-based social networks have been gaining increasing popularity in recent years. To increase users' engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupon delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this paper we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution. The advantages of modeling the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is 5.45 times smaller, while the prediction accuracy is 13.21% higher.
All Author(s) ListYe J., Zhu Z., Cheng H.
Name of ConferenceSIAM International Conference on Data Mining, SDM 2013
Start Date of Conference02/05/2013
End Date of Conference04/05/2013
Place of ConferenceAustin
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by SIAM
Pages171 - 179
LanguagesEnglish-United Kingdom

Last updated on 2022-14-01 at 00:41