Inferring continuous dynamic social influence and personal preference for temporal behavior prediction
Refereed conference paper presented and published in conference proceedings


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AbstractIt is always attractive and challenging to explore the intricate behavior data and uncover people's motivations, preference and habits, which can greatly benefit many tasks including link prediction, item recommendation, etc. Traditional work usually studies people's behaviors without time information in a static or discrete manner, assuming the underlying factors stay invariant in a long period. However, we believe people's behaviors are dynamic, and the contributing factors including the social influence and personal preference for behaviors are varying continuously over time. Such continuous dynamics convey important knowledge about people's behavior patterns; ignoring them would lead to inaccurate models. In this work, we address the continuous dynamic modeling of temporal behaviors. To model the fully continuous temporal dynamics of behaviors and the underlying factors, we propose the DP-Space, a dynamic preference probability space, which can capture their smooth variation in various shapes over time with flexible basis functions. Upon that we propose a generative dynamic behavior model, ConTyor, which considers the temporal item-adoption behaviors as joint effect of dynamic social influence and varying personal preference over continuous time. We also develop effective inference methods for ConTyor and present its applications. We conduct a comprehensive experimental study using real-world datasets to evaluate the effectiveness of our model and the temporal modeling. Results verify that ConTyor outperforms existing state-of-the-art static and temporal models in behavior predictions. Moreover, in our detailed study on temporal modeling, we show that temporal modeling is superior to static approaches and modeling over continuous time is further better than that over discrete time. We also demonstrate that the ancient behavior data can still become important and beneficial if modeled well. © 2014 VLDB Endowment 21508097/14/11.
All Author(s) ListZhang J., Wang C., Wang J., Yu J.X.
Name of Conference3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Start Date of Conference11/09/2006
End Date of Conference11/09/2006
Place of ConferenceSeoul
Country/Region of ConferenceSouth Korea
Detailed descriptionorganized by PVLDB,
Year2014
Month1
Day1
Volume Number8
Issue Number3
Pages269 - 280
ISSN2150-8097
LanguagesEnglish-United Kingdom

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