NDLI: Predicting user activity level in social networks

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Predicting user activity level in social networks

Content Provider	ACM Digital Library
Author	Wang, Xiao Zhong, Erheng Pan, Sinno Jialin Zhou, Minzhe Zhu, Yin Yang, Qiang
Abstract	The study of users' social behaviors has gained much research attention since the advent of various social media such as Facebook, Renren and Twitter. A major kind of applications is to predict a user's future activities based on his/her historical social behaviors. In this paper, we focus on a fundamental task: to predict a user's future activity levels in a social network, e.g. weekly activeness, active or inactive. This problem is closely related to Social Customer Relationship Management (Social CRM). Compared to traditional CRM, the three properties: user diversity, social influence, and dynamic nature of social networks, raise new challenges and opportunities to Social CRM. Firstly, the user diversity property implies that a global predictive model may not be precise for all users. On the other hand, historical data of individual users are too sparse to build precisely personalized models. Secondly, the social influence property suggests that relationships between users can be embedded to further boost prediction results on individual users. Finally, the dynamical nature of social networks means that users' behaviors may keep changing over time. To address these challenges, we develop a personalized and social regularized time-decay model for user activity level prediction. Experiments on the social media Renren validate the effectiveness of our proposed model compared with some baselines including traditional supervised learning methods and node classification methods in social networks.
Starting Page	159
Ending Page	168
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505518
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social network analysis User activity Prediction
Content Type	Text
Resource Type	Article

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