Social life networks: a multimedia problem?
Proceedings of the 21st ACM international conference on Multimedia
Exploring temporal effects for location recommendation on location-based social networks
Proceedings of the 7th ACM conference on Recommender systems
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Location context in social media plays an important role in many applications. In addition to explicit location sharing via popular â聙聹check inâ聙聺 service, user-posted content could also implicitly reveals usersâ聙聶 location context. Identifying such a location context based on content is an interesting problem because it is not only important in inferring social ties between people, but also vital for applications such as user profiling and targeted advertising. In this paper, we study the problem of location type classification using tweet content. We extend probabilistic text classification models to incorporate temporal features and user history information in terms of probabilistic priors. Experimental results show that our extensions can boost classification accuracy effectively.