This report documents the program and the outcomes of Dagstuhl Seminar 24211, "Evaluation Perspectives of Recommender Systems: Driving Research and Education", which brought together 41 participants from 16 countries.

The seminar brought together distinguished researchers and practitioners from the recommender systems community, representing a range of expertise and perspectives. The primary objective was to address current challenges and advance the ongoing discourse on the evaluation of recommender systems. The participants' diverse backgrounds and perspectives on evaluation significantly contributed to the discourse on this subject.

The seminar featured eight presentations on current challenges in the evaluation of recommender systems. These presentations sparked the general discussion and facilitated the formation of groups around these topics. As a result, five working groups were established, each focusing on the following areas: theory of evaluation, fairness evaluation, best-practices for offline evaluations of recommender systems, multistakeholder and multimethod evaluation, and evaluating the long-term impact of recommender systems.@en

This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.@en