Qi Tang
ABSTRACT
Customer churn prediction is an active research topic for the data mining community and business managers in this rapidly growing society. The ability to detect churn customers precisely is something that every company would wish to achieve. With the great success of DNNs, several churn prediction models based on DNNs are proposed in recent years. However, traditional DNNs cannot learn high-order feature interactions and deal with one-hot vectors well. In this paper, we proposed a feature interaction network (FIN), which aims to enhance the inherent relations of discrete features and learn high-order feature interactions. This network contains two modules: an entity embedding network and a factorization machine network with several sliding windows. From the experiments, it is observed that our proposed model has a better predictive performance than several state-of-the-art models on 4 public datasets.
- Stripling, E., Broucke, S., Antonio, K., Baesens, B., and Snoeck, M. 2018. Profit Maximizing Logistic Model for Customer Churn Prediction Using Genetic Algorithms. Swarm and Evolutionary Computation 40, 116--130.Google Scholar
Cross Ref
- Chitra, K. and Subashini, B. 2011. Customer Retention in Banking Sector using Predictive Data Mining Technique. ICIT 2011 the 5th International Conference on Information Technology, 1--4.Google Scholar
- Tang, L., Thomas, L., Fletcher, M., Pan, J., and Marshall, A. 2014. Assessing the Impact of Derived Behavior Information on Customer Attrition in the Financial Service Industry. European Journal of Operational Research 296, 624--633.Google ScholarCross Ref
- Sangar, A. B. and Rastari, S. 2015. A Model for Increasing Usability of Mobile Banking Apps on Smart Phones. Indian Journal of Science and Technology 8(November), 1--9.Google ScholarCross Ref
- Oskarsdottir, M., Bravo, C., Verbeke, W., Verbeke W., Baesens, B., and Vanthienen, J. 2016. A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 1151--1158.Google Scholar
- Verbekea, W., Martensb, D., and Baesens, B. 2014. Social Network Analysis for Customer Churn Prediction. Applied soft Computing 14, 431--446.Google Scholar
- Oskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., and Vanathien, J. 2017. Social Network Analytics for Churn Prediction in telco: Model Building, Evaluation and Network Architecture. Expert Systems with Applications 85, 204--220.Google ScholarDigital Library
- Berengueres, J. and Efimov, D. 1998. Airline New Customer Tier Level Forecasting for Real-time Resource Allocation of A Miles Program. Data Mining and Knowledge Discovery 2(2), 121--167.Google ScholarDigital Library
- Suznjevic, M., Matijasevic, M., and Matijasevic, M. 1998. MMORPG Player Behavior Model Based on Player Action Categories. Proceedings of the 10th Annual Workshop on Network and Systems Support for Games, 1--6.Google Scholar
- Hadiji, F., Sifa, R., Thurau, C., Drachen, A., Kersting, K. and Bauckhage, C. 2014. Predicting player churn in the wild. IEEE Conference on Computational Intelligence and Games.Google Scholar
- Moeyersoms, J. and Martens, D. 2015. Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decision Support Systems 72, 72--81.Google ScholarDigital Library
- Amin, A., Anwar, S., Adnan, A., Nawaz, M., and Alawfi, K. 2017. Customer Churn Prediction in the Telecommunication Sector using a Rough Set Approach. Neurocomputing 237, 242--254.Google ScholarDigital Library
- Yu, R., An, X., Jin, B., Shi, J., and Move, O. A. 2018. Particle Classification Optimization-based BP network for Telecommunication Customer Churn Prediction. Neural Computing and Applications 29, 707--720.Google ScholarDigital Library
- Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., and Anwar, S. 2019. Customer Churn Prediction in Telecommunication Industry using Data Certainty. Journal of Business Research 94, 290--301.Google ScholarCross Ref
- Wang, Z., Xiao, W., and Wang, J. 2018. A Practical Pipeline with Stacking Models for KKBOX's Churn Prediction Challenge. The 11th ACM International Conference on Web Search and Data Mining (WSDM).Google Scholar
- Brandusoiu, I. and Toderean, G. 2013. Churn Prediction in the Telecommunications Sector using Support Vector Machines. Annals of the Oradea University.Google Scholar
- Da, Y., Ge, X. 2005. An Improved PSO-based ANN with Simulated Annealing Technique. Neurocomputing 63, 527--533.Google ScholarDigital Library
- Chen, T. and Guestrin, C. 2016. XGBoost: A Scalable Tree Boosting System, arXiv preprint arXiv: 1603.02754.Google Scholar
- Caigny, A. D., Coussement, K., and Bock, K. W. D. 2018. A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Tree. European Journal of Operational Research 269, 760--772.Google ScholarCross Ref
- Yang, C., Shi, X., Luo, J., and Han, J. 2018. I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application, Knowledge Discovery in Database (KDD), 2018, 914--922.Google Scholar
- Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., and Sun, G. 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommenders Systems, arXiv preprint arXiv: 1803.05170,.Google Scholar
- Jenatton, R., Roux, N. L., Bordes, A., and Obozinski, G. R. 2012. A Latent Factor Model for Highly Multi-relational Data, Neural Information Processing Systems, 3167--3175.Google Scholar
- Rendle, S. 2010. Factorization Machines, IEEE International Conference on Data Mining, 995--1000.Google ScholarDigital Library
- Mikolov, T., Deoras, A., Kombrink, S., Burget, L., Cernocky, J. H. 2011. Empirical Evaluation and Combination of Advanced Language Modeling Techniques, The12th Annual Conference of the International Speech Communication Association, 605--608.Google ScholarCross Ref
- Clevert, D., Unterthiner, T., and Hochreiter, S. 2016. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUS), International Conference on Learning Representations.Google Scholar
- Kingma, D. P. and Ba, J. L. 2015. ADAM: A Method For Stochastic Optimization, International Conference on Learning Representation.Google Scholar
- Pan, W., Liu, Z., Ming, Z., Zhong, H., Wang, X., and Xu, C. 2015. Compressed Knowledge Transfer via Factorization Machine for Heterogeneous Collaborative Recommendation, Knowledge-based Systems 85, 234--244.Google ScholarDigital Library
- Guo, C. and Berkhahn, F. 2016. Entity Embedding of Categorical Variables, arxiv preprint arxiv: 1604.06737v.Google Scholar
- Rogier, A., Donders, T., Heijden, G. J. M. G., Stijnen, T., and Moons, K. G. M. 2006. Review: A Gentle Introduction to Imputation of Missing Values, Journal of Clinical Epidemiology 59, 1087--1091.Google ScholarCross Ref
- Japkowicz, N. and Stephen, S. 2002. The Class Imbalance Problem: A Systematic Study, Intelligent Data Analysis 6, 429--449.Google ScholarDigital Library
- Davic, J. and Goadrich, M. 2006. The Relationship Between Precision-Recall and ROC Curves, The 23 International Conference on Machine Learning, 233--240.Google Scholar
Index Terms
- A Feature Interaction Network for Customer Churn Prediction
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