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

Group recommender systems aim to suggest appropriate products/services to a group of
users rather than individuals. These recommendations rely solely on determining group …

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 …

Popularity bias is an undesirable phenomenon associated with recommendation algorithms
where popular items tend to be suggested over long-tail ones, even if the latter would be of …

Group recommender systems are based on aggregation heuristics that help to determine a
recommendation for a group. These heuristics aggregate the preferences of individual users …

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

Recommender Systems (RecSys) provide suggestions in many decision-making processes.
Given that groups of people can perform many real-world activities (eg, a group of people …

Recommender-systems have been a significant research direction in both literature and
practice. The core of recommender systems are the recommendation mechanisms, which …

This chapter addresses the topic of group recommendations, how a system can recommend
to a group of users. This topic has received a considerable attention, especially in recent …

Group Recommendation (GR) is the task of suggesting relevant items/events for a group of
users in online systems, whose major challenge is to aggregate the preferences of group …