  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.
