  Session-based recommendation is a challenging problem in the real-world
scenes, e.g., ecommerce, short video platforms, and music platforms, which aims
to predict the next click action based on the anonymous session. Recently,
graph neural networks (GNNs) have emerged as the state-of-the-art methods for
session-based recommendation. However, we find that there exist two limitations
in these methods. One is the item transition relations are not fully exploited
since the relations are not explicitly modeled. Another is the long-range
dependencies between items can not be captured effectively due to the
limitation of GNNs. To solve the above problems, we propose a novel approach
for session-based recommendation, called Transition Relation Aware
Self-Attention (TRASA). Specifically, TRASA first converts the session to a
graph and then encodes the shortest path between items through the gated
recurrent unit as their transition relation. Then, to capture the long-range
dependencies, TRASA utilizes the self-attention mechanism to build the direct
connection between any two items without going through intermediate ones. Also,
the transition relations are incorporated explicitly when computing the
attention scores. Extensive experiments on three real-word datasets demonstrate
that TRASA outperforms the existing state-of-the-art methods consistently.
