With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

In recent years, the use of recommender systems has become popular on the web. To
improve recommendation performance, usage, and scalability, the research has evolved by …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

With tremendous amount of recommendation algorithms proposed every year, one critical
issue has attracted a considerable amount of attention: there are no effective benchmarks for …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

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 …

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

The recommendation system is an important and widely used technology in the era of Big
Data. Current methods have fused side information into it to alleviate the sparsity problem …

Most existing studies on next location recommendation propose to model the sequential
regularity of check-in sequences, but suffer from the severe data sparsity issue where most …

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 …