For a long time, the problems of cold start and sparse data have always been the key
problems to be solved by the recommendation system. Researchers usually use auxiliary …

Conventionally, cold-start limitations are managed by leveraging side information such as
social-trust relationships. However, the relationships between users in social networks are …

Most existing recommenders focus on providing users with a list of recommended products.
In practical, users may only pay attention to the recommendations at the top positions. Our …

Knowledge-based recommendation models effectively alleviate the data sparsity issue
leveraging the side information in the knowledge graph, and have achieved considerable …

Integrating social relations into recommendation is an effective way to mitigate data sparsity.
Most social recommendation methods encode user representations from a unified graph that …

As knowledge graphs have attracted enormous attention from researchers, much effort has
been invested in recommendation systems to mine user preferences effectively. In particular …

Recently, recommendation systems have been widely usedin online business scenarios,
which can improve the online experience by learning the user or item characteristics to …

Sequential recommendation algorithm can predict the next action of a user by modeling the
user's interaction sequence with an item. However, most sequential recommendation …

Prerequisites can play a crucial role in users' decision-making yet recommendation systems
have not fully utilized such contextual background knowledge. Traditional recommendation …

Recommender systems usually encounter the issue of sparse interaction data, which is
commonly alleviated by social recommendation models based on graph neural networks …