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 …

Course recommender systems play an increasingly pivotal role in the educational
landscape, driving personalization and informed decision-making for students. However …

Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …

To provide precise recommendations, traditional recommender systems (RS) collect
personal data, user preference and feedback, which are sensitive to each user if such …

In this paper, we develop a novel hybrid recommender system for the tourism domain, which
combines (a) a Bayesian preferences elicitation component which operates by asking the …

The personalized product preference and decision-making recommendation systems are
highly demanded to handle big data and to increase service quality of the e-commerce …

Recommender systems are tools that help users in the decision-making process of choosing
items that may be relevant for them among a vast amount of other items. One of the main …

Research on the problem of new user cold-start recommendation generally leverages user
side information to suggest items to new users. This approach, however, is impractical due …

Recommendation systems are widely used to filter massive information. However, they often
face the challenges of cold start and sparsity problems, limiting their effectiveness. Bayesian …

With the rapid popularization of the mobile Internet, micro-video has become the mainstream
media form today. Compared with traditional video, micro-videos have the characteristics of …