I. Introduction
Personalized recommendation methods have been deployed in many applications [1], [2] to solve information overload and refine user experience in online services. Collaborative filtering (CF) [3] is the mainstream algorithm for recommender systems because of its effectiveness and low computational overload. At its core is using historical user–item interactions to incorporate collaborative signals into the embedding process.