Recommendation Systems (RS) save the time of users in their hectic life schedules for
purchasing their interested products. RS faces challenges of data sparsity, cold start …

In this paper, an integrated recommendation approach using Radial Basis Function Network
(RBFN) and Collaborative Filtering (CF) is proposed. Radial basis function network is a …

User Reviews in the form of ratings giving an opportunity to judge the user interest on the
available products and providing a chance to recommend new similar items to the …

A recommender system (RS) is a subcategory of an information filtering system that attempts
the prediction of the score or the importance given to an item by a user. RS has garnered the …

With the enormous growth in the volume of online data, users are flooded with a gigantic
amount of information. This has made the task of Recommender systems (RSs) even more …

In today's world, recommendation systems assist people in suggesting the greatest item
among the most regularly used goods or the best item with a high level of popularity. The …

There are various libraries that facilitate the design and development of recommender
systems (RSs) research in both the academia and industry. Different libraries provide a …

Recommender system (RS) is an emerging technique in information retrieval to handle a
large amount of online data effectively. It provides recommendation to the online user in …

Collaborative filtering (CF) algorithms have been widely used to build recommender
systems since they have distinguishing capability of sharing collective wisdoms and …

Recommender Systems apply machine learning and data mining techniques for filtering
unseen information and can predict whether a user would like a given resource. This paper …