Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

TL;DR

This paper introduces GRec, a novel encoder-decoder framework that leverages future user interaction data during training to improve session-based recommendation accuracy, addressing limitations of existing methods.

Contribution

The paper proposes GRec, a gap-filling based model that effectively incorporates future context during training without data leakage, enhancing recommendation performance.

Findings

GRec outperforms state-of-the-art methods on real-world datasets.

Modeling future context improves recommendation accuracy.

Empirical results validate the utility of future data in training.

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.