Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …

The performance of recommender systems highly impacts both music streaming platform
users and the artists providing music. As fairness is a fundamental value of human life, there …

Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

With the advancement in recommendation techniques, focus is diverted from just making
them more accurate to making them fairer and diverse, thus catering to the set of less …

In today's digital landscape, recommender systems have gained ubiquity as a means of
directing users toward personalized products, services, and content. However, despite their …

Recommendation models are usually trained on observational data. However, observational
data exhibits various bias, such as popularity bias. Biased data results in a gap between …

Using AI technology to improve teaching and learning is an important goal of educational
sustainability. By mining the correlation between knowledge points, the discrete knowledge …

Recommendation systems today exert a strong influence on consumer behavior and
individual perceptions of the world. By using collaborative filtering (CF) methods to create …

Recommender System (RS) is an information filtering approach that helps the overburdened
user with information in his decision making process and suggests items which might be …