The task is to predict the chances of a user listening to a song repetitively after the first observable listening event within a time window was triggered. If there are recurring listening event(s) triggered within a month after the user's very first observable listening event, its target is marked 1, and 0 otherwise in the training set. KKBox provides a training data set consists of information of the first observable listening event for each unique user-song pair within a specific time duration. Metadata of each unique user and song pair is also provided. The train and the test data are selected from users listening history in a given time period, and are split based on time. Note that only the labeled train set of the dataset is used for benchmarking.
Variants: KKBox
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Click-Through Rate Prediction | FCN | FCN: Fusing Exponential and Linear … | 2024-07-18 |
| Click-Through Rate Prediction | DCNv2 | DCN V2: Improved Deep & … | 2020-08-19 |
| Click-Through Rate Prediction | AutoInt+ | AutoInt: Automatic Feature Interaction Learning … | 2018-10-29 |
| Click-Through Rate Prediction | xDeepFM | xDeepFM: Combining Explicit and Implicit … | 2018-03-14 |
| Click-Through Rate Prediction | DeepFM | DeepFM: A Factorization-Machine based Neural … | 2017-03-13 |
Recent papers with results on this dataset: