Item-based collaborative filtering has been widely used in practice and is becoming the most
promising approach in recommender systems. It predicts a user's interest for a target item …

Collaborative filtering has been successfully implemented in many commercial
recommender systems. These systems recommend items favored by other users with similar …

Item-based filtering technique is a collaborative filtering algorithm for recommendations.
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …

Collaborative filtering is one of widely used recommendation approaches to make
recommendation services for users. The core of this approach is to improve capability for …

Previous studies on collaborative filtering have mainly adopted local resources as the basis
for conducting analyses, and user rating matrices have been used to perform similarity …

The recommender system is widely used in the field of e-commerce and plays an important
role in guiding customers to make smart decisions. Although many algorithms are available …

Collaborative filtering recommendation algorithm is one of the most successful technologies
for building recommender systems. However, a user–based collaborative filtering method …

Similarity measures play an important role in the accuracy of collaborative filtering based
recommendation. Due to non-availability of adequate co-rated users, the accuracy of …

Similarity method is the key of the user-based collaborative filtering recommend algorithm.
The traditional similarity measures, which cosine similarity, adjusted cosine similarity and …

Similarity-based algorithms, often referred to as memory-based collaborative filtering
techniques, are one of the most successful methods in recommendation systems. When …