论文标题
电信公司流失预测的社交网络分析:模型构建,评估和网络体系结构
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
论文作者
论文摘要
社交网络分析方法正在电信行业中使用,以预测客户流失。特别是已经表明,适合此特定问题的关系学习者增强了预测模型的性能。 在当前的研究中,我们通过将共同学习者应用于八个不同的呼叫记录数据集,基于构建关系学习者的不同策略,源自全球电信组织。我们统计地评估了关系分类者和集体推理方法对关系学习者的预测能力的影响,以及关系学习者与传统方法相结合的模型的性能,以预测电信行业中客户流失的传统方法。 最后,我们研究了网络构建对模型性能的影响;我们的发现表明,网络中边缘和权重的定义确实会影响预测模型的结果。研究的结果是,最好的配置是使用网络变量富含网络变量的非关系学习者,而没有集体推断,使用二进制权重和无方向的网络。此外,我们还提供有关如何以最佳方式应用社交网络分析在电信行业中进行搅拌预测的准则,从网络架构到模型构建和评估。
Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.