Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
arXiv:1906.04473
Abstract
Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training.Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it. However, we argue that the future interactions after a target interaction, which are also available during training, provide valuable signal on user preference and can be used to enhance the recommendation quality. Properly integrating future data into model training, however, is non-trivial to achieve, since it disobeys machine learning principles and can easily cause data leakage. To this end, we propose a new encoder-decoder framework named Gap-filling based Recommender (GRec), which trains the encoder and decoder by a gap-filling mechanism. Specifically, the encoder takes a partially-complete session sequence (where some items are masked by purpose) as input, and the decoder predicts these masked items conditioned on the encoded representation. We instantiate the general GRec framework using convolutional neural network with sparse kernels, giving consideration to both accuracy and efficiency. We conduct experiments on two real-world datasets covering short-, medium-, and long-range user sessions, showing that GRec significantly outperforms the state-of-the-art sequential recommendation methods. More empirical studies verify the high utility of modeling future contexts under our GRec framework.
References in corpus (15)
- Adam: A Method for Stochastic Optimization
- Sequence to Sequence Learning with Neural Networks
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Convolutional Sequence to Sequence Learning
- Conditional Image Generation with PixelCNN Decoders
- Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
- MASS: Masked Sequence to Sequence Pre-training for Language Generation
- Session-based Recommendations with Recurrent Neural Networks
- Adversarial Personalized Ranking for Recommendation
- Neural Machine Translation in Linear Time
- A Survey on Session-based Recommender Systems
- Next Item Recommendation with Self-Attention
- Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition
- Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
- Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks