Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

Collaborative filtering models based on matrix factorization and learned similarities using
Artificial Neural Networks (ANNs) have gained significant attention in recent years. This is, in …

This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to
handle the recommendation problem under cold-start settings. Specifically, we divide the …

Recent popularity surrounds large AI language models due to their impressive natural
language capabilities. They contribute significantly to language-related tasks, including …

Deep Learning and factorization-based collaborative filtering recommendation models have
undoubtedly dominated the scene of recommender systems in recent years. However …

Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …

Collaborative filtering models have undoubtedly dominated the scene of recommender
systems in recent years. However, due to the little use of content information, they narrowly …

Most online service providers utilize a recommender system to help their customers'
decision making process by producing referrals. If a customer requests a suggestion for a …

In the last few years, a renewed interest of the research community in conversational
recommender systems (CRSs) has been emerging. This is likely due to the massive …

The person-job fit algorithm has become a crucial task in the online recruitment industry for
matching resumes with suitable jobs and making recommendations. However, individuals' …