  Sequential recommendation task aims to predict user preference over items in
the future given user historical behaviors. The order of user behaviors implies
that there are resourceful sequential patterns embedded in the behavior history
which reveal the underlying dynamics of user interests. Various sequential
recommendation methods are proposed to model the dynamic user behaviors.
However, most of the models only consider the user's own behaviors and
dynamics, while ignoring the collaborative relations among users and items,
i.e., similar tastes of users or analogous properties of items. Without
modeling collaborative relations, those methods suffer from the lack of
recommendation diversity and thus may have worse performance. Worse still, most
existing methods only consider the user-side sequence and ignore the temporal
dynamics on the item side. To tackle the problems of the current sequential
recommendation models, we propose Sequential Collaborative Recommender (SCoRe)
which effectively mines high-order collaborative information using
cross-neighbor relation modeling and, additionally utilizes both user-side and
item-side historical sequences to better capture user and item dynamics.
Experiments on three real-world yet large-scale datasets demonstrate the
superiority of the proposed model over strong baselines.
