Session-based Sequential Skip Prediction via Recurrent Neural Networks
arXiv:1902.04743
Abstract
The focus of WSDM cup 2019 is session-based sequential skip prediction, i.e. predicting whether users will skip tracks, given their immediately preceding interactions in their listening session. This paper provides the solution of our team \textbf{ekffar} to this challenge. We focus on recurrent-neural-network-based deep learning approaches which have previously been shown to perform well on session-based recommendation problems. We show that by choosing an appropriate recurrent architecture that properly accounts for the given information such as user interaction features and song metadata, a single neural network could achieve a Mean Average Accuracy (AA) score of 0.648 on the withheld test data. Meanwhile, by ensembling several variants of the core model, the overall recommendation accuracy can be improved even further. By using the proposed approach, our team was able to attain the 1st place in the competition. We have open-sourced our implementation at GitHub.
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- A Neural Representation of Sketch Drawings
- Wide & Deep Learning for Recommender Systems
Cited by in corpus (4)
- Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
- Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning
- MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
- Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading