SSR: Explainable Session-based Recommendation

In recent years, session-based recommendation has attracted more and more attention. Previous work utilizing deep learning approaches has achieved significant progress in the accuracy of prediction. However, why the user would view the next item is not clear, though attention mechanism assigns different importance on items in a session. Meanwhile, some traditional data mining-based methods are able to provide explanations, but they mainly offer a narrow perspective like measuring item similarity and fail to achieve as good performance as deep learning-based approaches. To this end, we propose an explainable session-based recommendation by considering three factors including sequential patterns, repetition clicks, and item similarity. Concretely, we design a two-stage framework where candidate items are firstly selected according to users' sequential patterns and repeated behaviors, and then they are further ranked by considering short-term interest with the calculation of item similarity. The experimental results on two benchmark datasets show that our model not only achieves competitive recommendation performance with advanced deep learning based models, but also provides reasonable explanations.