The increasing ubiquity of recommendation systems in modern applications has brought
about significant changes in various aspects of our daily lives. However, the emergence of …

In the realm of news recommendations, the persistent challenge of the cold-start problem
continues to impede progress. Existing approaches rely heavily on information exchange …

The cold-start problem is a long-standing problem in recommender systems, ie, lack of
historical interaction information hinders effective recommendations for new users and …

Predicting potential user behaviors is of great importance in recommendation systems.
Existing behavior prediction works mainly aim to construct the user behavior preference …

News recommendation systems heavily rely on the information exchange between news
articles and users to personalize the recommendation. Consequently, one of the significant …

The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …

We consider the cold-start task for new users of a recommender system, whereby a new
user is asked to rate a few items with the aim of quickly discovering the user's preferences …

Many strategies have been developed to mine critical nodes in the networks, however, most
of them are based solely on the network topology, with no regard for the diversity of …

Social recommendation (SR) based on Graph Neural Networks (GNNs) presents a
promising avenue to significantly improve user experience by leveraging historical behavior …

Automatic worker recommendation has become a key technology in knowledge-intensive
crowdsourcing (KIC). However, KIC recommendation encounters the task cold-start problem …