In big data era, collaborative filtering as one of the most popular recommendation
techniques plays an important role to promote the development of online trade. Similarity …

In collaborative filtering-based recommender systems, items are recommended by
consulting ratings of similar users. However, if the number of ratings to compute similarity is …

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 item-based collaborative filtering technique recommends an item to the user from the
rating of k-nearest items. Generally, a random value of k is considered to find nearest …

Collaborative filtering (CF) is the most successful approach for personalized product or
service recommendations. Neighborhood based collaborative filtering is an important class …

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 …

Recommender system has been popularly used for recommending products and services to
the online buyers and users. Collaborative Filtering (CF) is one of the most popular filtering …

Similarity is a vital component for neighborhood-based collaborative filtering (CF). To
improve the quality of recommendation, many similarity methods have been proposed and …

Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …

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 …