  This paper explores providing explainability for session-based recommendation
(SR) by path reasoning. Current SR models emphasize accuracy but lack
explainability, while traditional path reasoning prioritizes knowledge graph
exploration, ignoring sequential patterns present in the session history.
Therefore, we propose a generalized hierarchical reinforcement learning
framework for SR, which improves the explainability of existing SR models via
Path Reasoning, namely PR4SR. Considering the different importance of items to
the session, we design the session-level agent to select the items in the
session as the starting point for path reasoning and the path-level agent to
perform path reasoning. In particular, we design a multi-target reward
mechanism to adapt to the skip behaviors of sequential patterns in SR, and
introduce path midpoint reward to enhance the exploration efficiency in
knowledge graphs. To improve the completeness of the knowledge graph and to
diversify the paths of explanation, we incorporate extracted feature
information from images into the knowledge graph. We instantiate PR4SR in five
state-of-the-art SR models (i.e., GRU4REC, NARM, GCSAN, SR-GNN, SASRec) and
compare it with other explainable SR frameworks, to demonstrate the
effectiveness of PR4SR for recommendation and explanation tasks through
extensive experiments with these approaches on four datasets.
