solved As online platforms are striving to get more users, a

As online platforms are striving to get more users, a critical chal- lenge is user churn, which is especially concerning for new users. In this paper, by taking the anonymous large-scale real-world data from Snapchat as an example, we develop ClusChurn, a system- atic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that proper user clustering can help understand and predict user churn. Therefore, ClusChurn firstly groups new users into interpretable typical clusters, based on their activities on the platform and ego-network structures. Then we design a novel deep learning pipeline based on LSTM and at- tention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users’ multi- dimensional activities and the underlying user types. ClusChurn is also able to predict user types, which enables rapid reactions to dif- ferent types of user churn. Extensive data analysis and experiments show that ClusChurn provides valuable insight into user behaviors, and achieves state-of-the-art churn prediction performance. The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. It is also general enough to be readily adopted by any online systems with user behavior data.Describe the development of ClusChurn system.Define ChurnHow did the authors understand user types, what challenges do they encounter?What are the main contributions of the authors’ work?Describe the dataset they used?What were the two typed of features associated with the users?What were the challenges for automatically finding interpretable clustering of users?With the new user churn prediction with high accuracy and limited data, why was it challenging?What metrics were used to compute churn prediction and multi-class classification?Describe how the author split the data?Given this paper focuses on Snapchat data as comprehensive example, how can the used techniques be used for future projects?