Explainable Session-based Recommendation with Meta-path Guided Instances and Self-Attention Mechanism

Session-based recommendation (SR) gains increasing popularity because it helps greatly maintain users' privacy. Aside from its efficacy, explainability is also critical for developing a successful SR model, since it can improve the persuasiveness of the results, the users' satisfaction, and the debugging efficiency. However, the majority of current SR models are unexplainable and even those that claim to be interpretable cannot provide clear and convincing explanations of users' intentions and how they influence the models' decisions. To solve this problem, in this research, we propose a meta-path guided model which uses path instances to capture item dependencies, explicitly reveal the underlying motives, and illustrate the entire reasoning process. To begin with, our model explores meta-path guided instances and leverages the multi-head self-attention mechanism to disclose the hidden motivations beneath these path instances. To comprehensively model the user interest and interest shifting, we search paths in both adjacent and non-adjacent items. Then, we update item representations by incorporating the user-item interactions and meta-path-based context sequentially. Compared with recent strong baselines, our method is competent to the SOTA performance on three datasets and meanwhile provides sound and clear explanations.