WSDM Cup 2018: Music Recommendation and Churn Prediction

Recommendation systems facilitate users retrieving contents they might like but not aware of yet. Furthermore, an effective recommendation system can potentially increase users' retention and conversion rate. One critical challenge for building a recommender system lies in the existence of cold start cases when we have sparse records for certain users or items: without enough rating data about a new song or a new user, it is necessary to rely on auxiliary information to perform effective recommendation. In the first task of WSDM Cup 2018, we challenge the participants to solve the abovementioned challenges in building a music recommendation system. The 2nd task of the Cup focuses on churn prediction. For a subscription business, accurately predicting churn is critical to its long-term success as even a slight variation in churn can significantly affect the profits. In this task, participants are asked to build an algorithm that predicts whether a user will churn after their subscription expires. The competition data and award are provided by KKBOX, a leading music streaming service company from Taiwan.