Explaining Recommendations in Heterogeneous Networks
Azin Ghazimatin · 2020
Users are increasingly relying on personalized recommendations (such as news, songs, products) for their daily information consumption. To deliver personalized content to users, Heterogeneous Information Network (HIN)-based recommender systems integrate various data collected from users into an often complex ranking model. The resulting recommendations might thus be puzzling for the users, leaving them wondering why some particular items are recommended to them or how these items relate to their actions on the platform. Therefore, to gain users' trust, it is crucial to provide them with explanations for their recommendations.