Graph-based Extractive Explainer for Recommendations
Peng Wang, Renqin Cai, Hongning Wang · Proceedings of the ACM Web Conference 2022 · 2022
Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time.