With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

A random walk is known as a random process which describes a path including a
succession of random steps in the mathematical space. It has increasingly been popular in …

Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is
attributed to their capability on learning good user and item embeddings by exploiting the …

Learning vector representations (aka. embeddings) of users and items lies at the core of
modern recommender systems. Ranging from early matrix factorization to recently emerged …

Traditional recommendation models that usually utilize only one type of user-item interaction
are faced with serious data sparsity or cold start issues. Multi-behavior recommendation …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Graph convolution networks (GCNs) for recommendations have emerged as an important
research topic due to their ability to exploit higher-order neighbors. Despite their success …

Collaborative filtering (CF) is one of the most popular and important recommendation
methodologies in the heart of numerous recommender systems today. Although widely …