Recommender systems (RS) are among the most widely used applications in data mining
and machine-learning technologies. These technologies recommend relevant products to …

Over the years, the recommender systems (RS) have witnessed an increasing growth for its
enormous benefits in supporting users' needs through map** the available products to …

Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …

User interactions with recommender systems (RSs) are affected by user selection bias, eg,
users are more likely to rate popular items (popularity bias) or items that they expect to enjoy …

Pursuing “intelligent justice” necessitates an impartial, productive, and technologically
driven methodology for judicial determinations. This scholarly composition proposes a …

Recommender systems are challenging research problems being exploited to suggest new
items or services, such as books, music and movies, and even people, to users based on …

Recommendation systems (RS) are crucial for social networking sites. Without it, finding
precise products is harder. However, existing systems lack adequate efficiency, especially …

Recommender systems are efficient tools for online applications; these systems exploit
historical user ratings on items to make recommendations of items to users. This paper aims …

The COVID‐19 crisis has once again highlighted the vulnerabilities of some critical areas in
cyberspace, especially in the field of education, as distance learning and social distance …

In real-world recommender systems, users' interest and products' characteristics tend to go
through a distinct series of changes over time. Thus, designing a recommender system that …