Collaborative filtering (CF) is typically used for recommending those items to a user which
other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and …

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 …

Recommender Systems (RS) have been being captured a great attraction of researchers by
their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is …

Collaborative Filtering (CF) is the most widely used prediction technique in recommender
systems. It makes recommendations based on ratings that users have assigned to items …

In today's society, time is considered more valuable than money, and researchers often have
limited time to find relevant papers for their research. Identifying and accessing essential …

Providing useful information to the users by recommending highly demanded products and
services is a fundamental part of the business of many top tier companies. Recommender …

ABSTRACT Recommendation Systems apply Information Retrieval techniques to select the
online information relevant to a given user. Collaborative Filtering (CF) is currently most …

Collaborative filtering (CF), one of the most widely employed methodologies for
recommender systems, has drawn undeniable attention due to its effectiveness and …

Collaborative filtering (CF) is widely used in recommendation systems. Traditional
collaborative filtering (CF) algorithms face two major challenges: data sparsity and …

With the rapid development and application of the mobile Internet, huge amounts of user
data are generated and collected every day. How to take full advantages of these ubiquitous …