  Recent research has achieved impressive progress in the session-based
recommendation. However, information such as item knowledge and click time
interval, which could be potentially utilized to improve the performance,
remains largely unexploited. In this paper, we propose a framework called
Knowledge-enhanced Session-based Recommendation with Temporal Transformer
(KSTT) to incorporate such information when learning the item and session
embeddings. Specifically, a knowledge graph, which models contexts among items
within a session and their corresponding attributes, is proposed to obtain item
embeddings through graph representation learning. We introduce time interval
embedding to represent the time pattern between the item that needs to be
predicted and historical click, and use it to replace the position embedding in
the original transformer (called temporal transformer). The item embeddings in
a session are passed through the temporal transformer network to get the
session embedding, based on which the final recommendation is made. Extensive
experiments demonstrate that our model outperforms state-of-the-art baselines
on four benchmark datasets.
