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

Collaborative filtering algorithms, such as matrix factorization techniques, are recently
gaining momentum due to their promising performance on recommender systems. However …

Collaborative filtering aims at predicting a user's interest for a given item based on a
collection of user profiles. This article views collaborative filtering as a problem highly …

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 …

Collaborative filtering is an effective recommendation approach in which the preference of a
user on an item is predicted based on the preferences of other users with similar interests. A …

Collaborative filtering aims at predicting a test user's ratings for new items by integrating
other like-minded users' rating information. The key assumption is that users sharing the …

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 (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …

The web contains a huge volume of data, and it's populating every moment to the point that
human beings cannot deal with the vast amount of data manually or via traditional tools …

Most collaborative filtering algorithms are based on certain statistical models of user
interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks …