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报告题目：Group Recommender Systems: Improving Explanaibility and Fairness

报告摘要：Today, most of the social media networks use automated tools to recommend content or products and to rank, curate and moderate posts. Recommender systems (RSs), and in particular Group recommender systems (GRSs), - a specific kind of RSs for recommending items to a group of users-, are likely to become more ubiquitous, with an expected forecast market to reach USD 16.13 billion by 2026.

These automated content governance tools are receiving an emerging interest as both the algorithms and the decision-making processes behind the platforms are not sufficiently transparent, with a negative impact in domains such as fair job opportunities, fair e-commerce or news exposure. Two of the key requirements that need to be fulfilled to building and maintaining users’ trust in AI systems and guarantee this transparency are Fairness and Explainability. But, beyond some previous attempts of boosting both aspects in traditional-individual RS, they have been hardly explored in GRSs.

This talk aims to provide different directions to address this challenge by novel algorithms and computational tools in GRS to boost explanation, fairness, and the synergy between them. In order to guarantee higher user’s trust, and independence of the RS output from any user’s socio-demographic feature.

报告人简介：Luis Martinez Lopez 教授是西班牙哈恩大学计算机科学系教授,主要研究方向为模糊决策、词计算、多准则决策等，目前担任 International Journal of Computational Intelligence Systems 主编，Information Sciences、Expert System with Applications、Knowledge-based System、IEEE Transactions on fuzzy systems、Information Fusion 等期刊副主编，IFSA 会士（IFSA Fellow），IEEE senior member，欧洲模糊逻辑与技术学会（ESFLT）会士。发表 270 余篇文章，2017-2023 年连续入选全球高被引学者榜单。