  Sequential recommendation (SR) plays an important role in personalized
recommender systems because it captures dynamic and diverse preferences from
users' real-time increasing behaviors. Unlike the standard autoregressive
training strategy, future data (also available during training) has been used
to facilitate model training as it provides richer signals about user's current
interests and can be used to improve the recommendation quality. However, these
methods suffer from a severe training-inference gap, i.e., both past and future
contexts are modeled by the same encoder when training, while only historical
behaviors are available during inference. This discrepancy leads to potential
performance degradation. To alleviate the training-inference gap, we propose a
new framework DualRec, which achieves past-future disentanglement and
past-future mutual enhancement by a novel dual network. Specifically, a dual
network structure is exploited to model the past and future context separately.
And a bi-directional knowledge transferring mechanism enhances the knowledge
learnt by the dual network. Extensive experiments on four real-world datasets
demonstrate the superiority of our approach over baseline methods. Besides, we
demonstrate the compatibility of DualRec by instantiating using RNN,
Transformer, and filter-MLP as backbones. Further empirical analysis verifies
the high utility of modeling future contexts under our DualRec framework. The
code of DualRec is publicly available at https://github.com/zhy99426/DualRec.
