Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".