As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

Nowadays, e-commerce is becoming an essential part of business for many reasons,
including the simplicity, availability, richness and diversity of products and services, flexibility …

During the last decades, recommender systems are becoming quite popular since they
provide great assistance to users on social networks and library websites. Unfortunately, the …

The Recommendation System (RS) is a very powerful technique which supports the user to
derive decision quickly. The RS is essential in any business model like B2B, B2C and C2C …

The traditional collaborative filtering algorithm obtains K nearest-neighbor items, which may
have the problem of too little similarity among nearest-neighbor items, making the accuracy …

Online learning presents a major challenge for learners, namely the diversification of
courses and information overload. In response to this issue, recommender systems are …

Intelligent Recommender Systems (RS) on conceptual learning of Computer Science (CS)
have been widely applied in the teaching of universities, in order to provide a personalized …

Abstract The Recommender System helps users find relevant content by leveraging their
interest models. This paper focuses on content-based recommender systems, which …

In this study, a restaurant recommendation system based on user-based collaborative
filtering was rigorously evaluated using the Yelp dataset. We developed a comprehensive …

This study aims to advance movie recommendation systems by integrating and comparing
multiple machine learning and deep learning algorithms, specifically focusing on Contact …