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WSDM Cup 2018

Authors :
Shou-De Lin
Yian Chen
Xing Xie
Arden Chiu
Source :
WSDM
Publication Year :
2018
Publisher :
ACM, 2018.

Abstract

Excellent recommendation system facilitates users retrieving contents they like and, what»s much more important - the contents they might like but they are not aware of yet. It will further increase the satisfaction of users and increase the retention rate and conversion rate indirectly. While the public's now listening to all kinds of music, recommendation algorithms still struggle in key areas. Without enough historical data, how would an algorithm know if listeners will like a new song or a new artist? And, how would it know what songs to recommend brand new users? In WSDM Cup 2018, the first task is to solve the abovementioned challenges to build a better music recommendation system. The 2nd task in the Cup focuses on churn prediction. For a subscription business, accurately predicting churn is critical to long-term success. Even slight variations in churn can drastically affect 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 in Taiwan.

Details

Database :
OpenAIRE
Journal :
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
Accession number :
edsair.doi...........cfd40c41de2d948a7234b6c266c6c263
Full Text :
https://doi.org/10.1145/3159652.3160605