Abstract—There is a common intuition that the user
behavioral pattern and social information of a group may
influence its attraction to users. In this paper, we employ user
behavioral and social information to predict the user quit rate of
social groups and validate the link between social behavioral
pattern and group quit rate on a large scale real dataset —
Tencent QQ groups. We routinely model this task as a
regression problem, and generate 97 features from user
behavioral and social information. Then we use an improved
Scalable Orthogonal Regression (iSOR) method to predict the
quit rate of QQ group. Our study shows that the quit rate of a
group can be predicted with high accuracy, furthermore, the
iSOR method selected several import features from the total 97
social behavioral features.
Index Terms—Quit rate, social group, social information,
user behavior.
The authors are with the Department of Computer Science and
Technology, Tsinghua University, Beijing 10084, China (e-mail:
tuc12@mails.tsinghua.edu.cn, cuip@tsinghua.edu.cn,
yangshq@tsinghua.edu.cn).
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Cite: Chang Tu, Peng Cui, and Shiqiang Yang, "Predict Quit Rate of Group from User Behavioral and Social Information," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 100-106, 2015.
