Feature selection is a crucial preprocessing step in the sphere of machine learning and data
mining, devoted to reducing the data dimensionality to improve the performance of learning …

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 …

E-commerce systems experience poor quality of performance when the number of records in
the customer database increases due to the gradual growth of customers and products …

Collaborative filtering is one of the most widely used recommendation system approaches.
One issue in collaborative filtering is how to use a similarity algorithm to increase the …

Due to their powerful personalization and efficiency features, recommendation systems are
being used extensively in many online environments. Recommender systems provide great …

Matrix Factorization (MF) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …

In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is
developed for recommendation systems with application in predicting baseline data of …

Traditional collaborative filtering based recommender systems deal with the two-
dimensional user-item rating matrix where users have rated a set of items into the system …

Complex and diverse information is flooding entire networks because of the rapid
development of mobile Internet and information technology. Under this condition, it is difficult …

In the past two decades, recommender systems have been successfully applied in many e-
commerce companies. One of the promising techniques to generate personalized …