The recommendation system is widely used in many aspects of digital economy to offer
personalized services, in which efficient capture of user–item relations is of critical …

Recommender systems have attracted widespread attention in various online applications.
To effectively recommend the needed items of users, knowledge graphs have been …

Because a knowledge graph's huge amount of item information can help recommender
systems develop user/item representations, it has become the most important source of side …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG)
for recommendation. Existing GNN-based methods explicitly model the dependency …

Online movies' recommender systems aim to address the information explosion of movies
and make the personalized recommendation for users. Recently, knowledge graphs have …

In recent years, the m ulti-task learning for k nowledge graph-based r ecommender system,
termed MKR, has shown its promising performance and has attracted increasing interest …

Recommender system can provide users with items that meet their potential needs in mass
information. Its development provides new ideas and supporting technologies for …

It is commonly agreed that a recommender system should use not only explicit information
(ie, historical user-item interactions) but also implicit information (ie, incidental information) …

Abstract Knowledge graph (KG) has proven to improve recommendation performance.
However, most efforts explore inter-entity relatedness by mining multi-hop relations on KG …