Predicting Customer Behavior in Telecommunications
IEEE Intelligent Systems
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Churn Prediction in MMORPGs: A Social Influence Based Approach
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Cautious Collective Classification
The Journal of Machine Learning Research
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Churn in Social Networks: A Discussion Boards Case Study
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Predicting user activity level in social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In service-based industries, churn poses a significant threat to the integrity of the user communities and profitability of the service providers. As such, research on churn prediction methods has been actively pursued, involving either intrinsic, user profile factors or extrinsic, social factors. However, existing approaches often address each type of factors separately, thus lacking a comprehensive view of churn behaviors. In this paper, we propose a new churn prediction approach based on collective classification (CC), which accounts for both the intrinsic and extrinsic factors by utilizing the local features of, and dependencies among, individuals during prediction steps. We evaluate our CC approach using real data provided by an established mobile social networking site, with a primary focus on prediction of churn in chat activities. Our results demonstrate that using CC and social features derived from interaction records and network structure yields substantially improved prediction in comparison to using conventional classification and user profile features only.