Personalized recommender systems aim to predict users' preferences for items. It has
become an indispensable part of online services. Online social platforms enable users to …

Since the 1990s, recommendation systems have been the subject of numerous studies. A
recommender system is a software tool that assists users in the choice-making process by …

Users have various behaviors on items, including page view, tag-as-favorite, add-to-cart,
and purchase in online shopping platforms. These various types of behaviors reflect users' …

Multi-round conversational recommendation (MCR), fulfilling a real–time recommendation
task for users through interactively asking attributes and recommending items, can be …

Dyna-style Model-based reinforcement learning (MBRL) methods have demonstrated
superior sample efficiency compared to their model-free counterparts, largely attributable to …

This paper presents a personalized recommendation system that suggests recipes to users
based on their health history and similar users' preferences. Specifically, the system utilizes …

With the rapid development of social networks, online and offline group activities are
becoming more common and diverse. Considering the different interests of group members …

Group recommendations are becoming prominent, since there are several applications in
which users form groups for activities (eg, tourism, restaurants, movies). SQUIRREL is a …

This paper addresses the aspects of fairness and transparency in Group Recommender
Systems (GRS), crucial for fostering user trust and system reliability. With Recommender …

Recommender systems are designed to help customers in finding their personalized
content. However, biases in recommender systems can potentially exacerbate over time …