Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …

With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Recommendation systems have become popular and effective tools to help users discover
their interesting items by modeling the user preference and item property based on implicit …

Cross-domain recommendation aims to leverage knowledge from multiple domains to
alleviate the data sparsity and cold-start problems in traditional recommender systems. One …