  In recommender systems, modeling user-item behaviors is essential for user
representation learning. Existing sequential recommenders consider the
sequential correlations between historically interacted items for capturing
users' historical preferences. However, since users' preferences are by nature
time-evolving and diversified, solely modeling the historical preference
(without being aware of the time-evolving trends of preferences) can be
inferior for recommending complementary or fresh items and thus hurt the
effectiveness of recommender systems. In this paper, we bridge the gap between
the past preference and potential future preference by proposing the
future-aware diverse trends (FAT) framework. By future-aware, for each
inspected user, we construct the future sequences from other similar users,
which comprise of behaviors that happen after the last behavior of the
inspected user, based on a proposed neighbor behavior extractor. By diverse
trends, supposing the future preferences can be diversified, we propose the
diverse trends extractor and the time-aware mechanism to represent the possible
trends of preferences for a given user with multiple vectors. We leverage both
the representations of historical preference and possible future trends to
obtain the final recommendation. The quantitative and qualitative results from
relatively extensive experiments on real-world datasets demonstrate the
proposed framework not only outperforms the state-of-the-art sequential
recommendation methods across various metrics, but also makes complementary and
fresh recommendations.
