Session-Based Recommendation Along with the Session Style of Explanation.

Explainability of recommendation algorithms is becoming an important characteristic in GDPR Europe. There are algorithms that try to provide explanations over graphs along with recommendations, but without focusing in user session information. In this paper, we study the problem of news recommendations using a heterogeneous graph and try to infer similarities between entities (i.e., sessions, articles, etc.) for predicting the next user click inside a user session. Moreover, we exploit meta paths to reveal semantic context about the session-article interactions and provide more accurate article recommendations along with robust explanations. We have experimentally compared our method against state-of-the-art algorithms on three real-life datasets. Our method outperforms its competitors in both accuracy and explainability. Finally, we have run a user study to measure the users’ satisfaction over different explanation styles and to find which explanations really help users to make more accurate decisions.