The effectiveness of recommendation systems heavily relies on accurately predicting user
ratings for items based on user preferences and item attributes derived from ratings and …

Context-aware recommender systems are intended primarily to consider the circumstances
under which a user encounters an item to provide better-personalized recommendations …

A fundamental task in the realms of computer vision, Low-Rank Matrix Recovery (LRMR)
focuses on the inherent low-rank structure precise recovery from incomplete data and/or …

The fast growth of the internet and communication technology in recent years has resulted in
rendering computers easily accessible to everyone. However, people have different …

Today, recommender systems are used in many different businesses to find items of interest
to users. The use of these systems is widely found in online economic systems and social …

In recommendation systems, the integration of multiple user-item features derived from
diverse datasets, such as rating data and review text, is essential for predicting user ratings …