  At the age of big data, recommender systems have shown remarkable success as
a key means of information filtering in our daily life. Recent years have
witnessed the technical development of recommender systems, from perception
learning to cognition reasoning which intuitively build the task of
recommendation as the procedure of logical reasoning and have achieve
significant improvement. However, the logical statement in reasoning implicitly
admits irrelevance of ordering, even does not consider time information which
plays an important role in many recommendation tasks. Furthermore,
recommendation model incorporated with temporal context would tend to be
self-attentive, i.e., automatically focus more (less) on the relevance
(irrelevance), respectively.
  To address these issues, in this paper, we propose a Time-aware
Self-Attention with Neural Collaborative Reasoning (TiSANCR) based
recommendation model, which integrates temporal patterns and self-attention
mechanism into reasoning-based recommendation. Specially, temporal patterns
represented by relative time, provide context and auxiliary information to
characterize the user's preference in recommendation, while self-attention is
leveraged to distill informative patterns and suppress irrelevances. Therefore,
the fusion of self-attentive temporal information provides deeper
representation of user's preference. Extensive experiments on benchmark
datasets demonstrate that the proposed TiSANCR achieves significant improvement
and consistently outperforms the state-of-the-art recommendation methods.
