The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

E-learning recommender systems are becoming more popular due to the massive learning
materials available online and the changing pedagogy. A content recommender system in …

For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

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

A recommender system (RS) aims to provide personalized recommendations to users for
specific items (eg, music, books). Popular techniques involve content-based (CB) models …

Commercial e-learning platforms have to overcome the challenge of resource overload and
find the most suitable material for educators using a recommendation system (RS) when an …

Recent advancements in Natural Language Processing (NLP) have led to the development
of NLP-based recommender systems that have shown superior performance. However …

Matrix factorization (MF) is one of the most popular techniques for product recommendation,
but is known to suffer from serious cold-start problems. Item cold-start problems are …

We propose a novel deep learning hybrid recommender system to address the gaps in
Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using …

The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …