Recommender systems are efficient tools for filtering online information, which is
widespread owing to the changing habits of computer users, personalization trends, and …

The explosive growth of online education environments is generating a massive volume of
data, specially in text format from forums, chats, social networks, assessments, essays …

A recommender system aims to provide users with personalized online product or service
recommendations to handle the increasing online information overload problem and …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

E-business leverages digital channels to scale its functions and services and operates by
connecting and retaining customers using marketing initiatives. To increase the likelihood of …

Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …

This paper presents a metric to measure similarity between users, which is applicable in
collaborative filtering processes carried out in recommender systems. The proposed metric …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

Recommender systems evaluate and filter the great amount of information available on the
Web, so they could be used in an academic environment to help users in their searches of …

Recommender systems are software tools and techniques for suggesting items in an
automated fashion to users tailored their preferences. Collaborative Filtering (CF) …