Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …

The tremendous expansion of information available on the web voraciously bombards
users, leaving them unable to make decisions and having no way of stepping back to …

In the literature, various collaborative filtering approaches have been developed to perform
an efficient recommendation on top of reducing the search cost of the customers. The …

An important factor affecting the performance of collaborative filtering for recommendation
systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the …

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

Data sparsity is a widespread problem of collaborative filtering (CF) recommendation
algorithms. However, some common CF methods cannot adequately utilize all user rating …

Abstract In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity
measures have been the operating component upon which CF performance is essentially …

A collaborative recommender system based on a latent factor model has achieved
significant success in the field of personalized recommender systems. However, the latent …

Collaborative filtering (CF), one of the most widely employed methodologies for
recommender systems, has drawn undeniable attention due to its effectiveness and …

The data sparsity is an acute challenge in most of the collaborative filterings (CFs) as their
performance is affected by the known ratings of target users. Recently, active learning has …