This innovative study introduces a novel enhancement to recommendation systems through
a synergistic integration of Collaborative Filtering (CF) and Content-Based Filtering (CBF) …

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has
been one of the priority interests of our society. There are many apps created to count …

A flood of information has occurred, making it challenging for people to find and filter their
favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; …

The interactions of users and items in recommender systems can be naturally modeled as
user-item bipartite graphs. The iterative propagation of graph neural networks (GNN) can …

Employing Artificial Intelligence techniques to address challenges in requirements elicitation
is gaining traction. Although nine systematic literature reviews have been published on AI …

Recommendation systems have lately gained popularity in a variety of applications due to
their ability to operate as information filters, thus delivering useful suggestions to users …

In recent years, recommender systems have become a crucial tool, assisting users in
discovering and engaging with valuable information and services. Multi-criteria …

The volume of healthcare information available on the internet has exploded in recent years.
Nowadays, many online healthcare platforms provide patients with detailed information …

Recommender systems have become increasingly important in today's digital age, but they
are not without their challenges. One of the most significant challenges is that users are not …

Collaborative filtering (CF) is an important approach for recommendation system which is
widely used in a great number of aspects of our life, heavily in the online-based commercial …