Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …

In the traditional content-based recommendation method, we usually use the movies users
watched before or rated to represent their profile. However, there are many movies that …

The recommender system (RS) can help us extract valuable data from a huge amount of raw
information. User-based collaborative filtering (UBCF) is widely employed in practical RSs …

Long-tail service recommendation provides an unexpected but reasonable experience for
potential developers when they construct mashups. However, the lack of available …

Recommender system (RS) suggests relevant objects to generate personalized service and
minimize information overload issue. User-based collaborative filtering (UBCF) plays a …

Point-of-interest (POI) recommendation has a wide range of application values in smart city
services computing. However, extreme sparsity of user-POI matrix seriously affects the …

Long-tail recommendations have gained significant attention owing to their potential
economic market. However, the scarcity of interaction data for long-tail users/items and the …

Long-tail recommendations have garnered considerable attention due to their potential
economic impact and their capability to offer unexpected yet reasonable experiences …

Social recommendation has received great attention recently, which uses social information
to alleviate the data sparsity problem and the cold-start problem of recommendation …

Graph neural networks have demonstrated impressive performance in the field of
recommender systems. However, existing graph neural network recommendation …