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Communication Dans Un Congrès Année : 2014

Incremental learning with social media data to predict near real-time events

Résumé

In this paper, we focus on the problem of predicting some particular user activities in social media. Our challenge is to consider real events such as message posting to friends or forwarding received ones, connecting to new friends, and provide near real-time prediction of new events. Our approach is based on latent factor models which can exploit simultaneously the timestamped interaction information among users and their posted content information. We propose a simple strategy to learn incrementally the latent factors at each time step. Our method takes only recent data to update latent factor models and thus can reduce computational cost. Experiments on a real dataset collected from Twitter show that our method can achieve performances that are comparable with other state-of-the-art non-incremental techniques.
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Dates et versions

hal-01185078 , version 1 (19-08-2015)

Identifiants

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Duc Kinh Le Tran, Cécile Bothorel, Pascal Cheung Mon Chan, Yvon Kermarrec. Incremental learning with social media data to predict near real-time events. DS 2014 : 17th International Conference on Discovery Science, Oct 2014, Bled, Slovenia. pp.180 - 191, ⟨10.1007/978-3-319-11812-3_16⟩. ⟨hal-01185078⟩
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