The remarkable achievements of Large Language Models (LLMs) have led to the
emergence of a novel recommendation paradigm—Recommendation via LLM (RecLLM) …

The recent growing concerns surrounding the pervasive adoption of generative AI can be
traced back to the long-standing influence of AI algorithms that have predominantly served …

Recommender systems learn from historical users' feedback that is often non-uniformly
distributed across items. As a consequence, these systems may end up suggesting popular …

Recommender System (RS) helps to find the items according to user interest and provides
various suggestions that help in the decision‐making process. These suggestions depend …

Educational recommender systems channel most of the research efforts on the effectiveness
of the recommended items. While teachers have a central role in online platforms, the impact …

When a recommender system suggests items to the end-users, it gives a certain exposure to
the providers behind the recommended items. Indeed, the system offers a possibility to the …

In the traditional content-based recommendation method, we usually use the movies users
watched before or rated to represent their profile. However, there are many movies that …

Modern user profiling approaches capture different forms of interactions with the data, from
user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural …

User modeling is a key topic in many applications, mainly social networks and information
retrieval systems. To assess the effectiveness of a user modeling approach, its capability to …

Online education platforms play an increasingly important role in mediating the success of
individuals' careers. Therefore, while building overlying content recommendation services, it …