  Sequential recommendation recommends items based on sequences of users'
historical actions. The key challenge in it is how to effectively model the
influence from distant actions to the action to be predicted, i.e., recognizing
the long-term dependence structure; and it remains an underexplored problem. To
better model the long-term dependence structure, we propose a GatedLongRec
solution in this work. To account for the long-term dependence, GatedLongRec
extracts distant actions of top-$k$ related categories to the user's ongoing
intent with a top-$k$ gating network, and utilizes a long-term encoder to
encode the transition patterns among these identified actions. As user intent
is not directly observable, we take advantage of available side-information
about the actions, i.e., the category of their associated items, to infer the
intents. End-to-end training is performed to estimate the intent representation
and predict the next action for sequential recommendation. Extensive
experiments on two large datasets show that the proposed solution can recognize
the structure of long-term dependence, thus greatly improving the sequential
recommendation.
