In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting
recommendation services. The conventional CF approach has limitations, namely data …

Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …

Recommender systems are software applications that provide product recommendations for
users based on their purchase history or ratings of items. The product recommendations are …

The explosive growth in the amount of available digital information in higher education has
created a potential challenge of information overload, which hampers timely access to items …

User-based collaborative filtering (UCF) technique is typically used to build a
recommendation system (RS). A wide variety of techniques, such as matrix factorization …

The increasing demand for personalized information has resulted in the development of the
Recommender System (RS). RS has been widely utilized and broadly studied to suggest the …

Collaborative Filtering (CF) has intrigued several researchers whose goal is to enhance
Recommender System's performance by mitigating their drawbacks. CF's common idea is to …

In designing modern recommender systems, item feature information (or side information) is
often ignored as most models focus on exploiting rating information. However, the side …

Collaborative filtering (CF) has successfully achieved application in personalized
recommendation systems. The singular value decomposition (SVD)++ algorithm is …

Abstract Recommendation Systems (RSs) are becoming tools of choice to select the online
information relevant to a given user. Collaborative Filtering (CF) is the most popular …