Nowadays, sequential recommendations are becoming more prevalent. A user expects the
system to remember past interactions and not conduct each recommendation round as a …

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in
terms of relevance has been the golden criterion for evaluating the quality of RS algorithms …

Recommender systems have been incorporated in our everyday life; from music to health
recommendations, recommender systems have enhanced the users' experience. At the …

Recommendation systems have received significant attention, with most of the proposed
methods focusing on personal recommendations. However, there are contexts in which the …

Recent research has employed reinforcement learning (RL) algorithms to optimize long-term
user engagement in recommender systems, thereby avoiding common pitfalls such as user …

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous
interests in developing RL-based recommender systems. In practical recommendation …

With the rapid development of online social networks, a growing number of people are
willing to share their group activities, eg having dinners with colleagues, and watching …

Recently, group recommendations have gained much attention. Nevertheless, most
approaches consider only one round of recommendations. However, in a real-life scenario …

Casting session-based or sequential recommendation as reinforcement learning (RL)
through reward signals is a promising research direction towards recommender systems …

Recommender systems generate recommendations based on user profiles, which consist of
past interactions of users with items. When user profiles are not available, session-based …