In recent years, the use of recommender systems has become popular on the web. To
improve recommendation performance, usage, and scalability, the research has evolved by …

In the era of internet access, recommender systems try to alleviate the difficulty that
consumers face while trying to find items (eg, services, products, or information) that better …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and
various cellular processes. The identification of disease-related miRNAs provides great …

Social networks have become very important for networking, communications, and content
sharing. Social networking applications generate a huge amount of data on a daily basis …

To quantify user–item preferences, a recommender system (RS) commonly adopts a high-
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …

In recent years, recommendation systems have been widely used in various commercial
platforms to provide recommendations for users. Collaborative filtering algorithms are one of …

Personalized recommender systems are playing an increasingly important role for online
consumption platforms. Because of the multitude of relationships existing in recommender …

Recommender system is an information filtering tool used to alleviate information overload
for users on the web. Collaborative filtering recommends items to users based on their …

Matrix Factorization is a successful approach for generating an effective recommender
system. However, most existing matrix factorization methods suffer from the sparsity and cold …