  Sequential recommendation focuses on mining useful patterns from the user
behavior history to better estimate his preference on the candidate items.
Previous solutions adopt recurrent networks or retrieval methods to obtain the
user's profile representation so as to perform the preference estimation. In
this paper, we propose a novel framework of sequential recommendation called
Look into the Future (LIFT), which builds and leverages the contexts of
sequential recommendation. The context in LIFT refers to a user's current
profile that can be represented based on both past and future behaviors. As
such, the learned context will be more effective in predicting the user's
behaviors in sequential recommendation. Apparently, it is impossible to use
real future information to predict the current behavior, we thus propose a
novel retrieval-based framework to use the most similar interaction's future
information as the future context of the target interaction without data
leakage. Furthermore, in order to exploit the intrinsic information embedded
within the context itself, we introduce an innovative pretraining methodology
incorporating behavior masking. This approach is designed to facilitate the
efficient acquisition of context representations. We demonstrate that finding
relevant contexts from the global user pool via retrieval methods will greatly
improve preference estimation performance. In our extensive experiments over
real-world datasets, LIFT demonstrates significant performance improvement on
click-through rate prediction tasks in sequential recommendation over strong
baselines.
