Year: 2024
Type: conference-paper
Abstract: In recent years, recommender systems have become indispensable tools in various domains, aiding users in discovering relevant content amidst the overwhelming amount of available material. However, the effectiveness and reliability of these systems are often hindered by various challenges such as data perturbations, missing data, noise, and bias. In this workshop, we aim to explore and address these challenges by focusing on the development of robust recommender systems. Robustness in recommender systems refers to their ability to maintain performance and effectiveness under adverse conditions, including unexpected variations in the data environment. By fostering discussions and collaborations among researchers and practitioners, this workshop seeks to advance the state-of-the-art in robust recommender systems, thereby enhancing their usability and trustworthiness in real-world applications.
Institutions: Università Campus Bio-Medico, Sapienza University of Rome
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403221344