ComPer: A Comprehensive Performance Evaluation Method for Recommender Systems
Bibliographic record
Abstract
RecommenderSystems are receiving substantial attention in several application areas (such as healthcare systems and e-commerce), where each area has different requirements.These systems are multifaceted by nature.So, many metrics, which are sometimes contradictious, are introduced to assess different aspects.The existence of several alternatives and dimensions to recommendation approaches complicate the evaluation of recommender systems.In such a situation, it is desirable to evaluate and compare recommenders in a united way that assesses the multifaceted aspects of these systems fairly and uniformly.Despite the abundance of evaluation dimensions, the literature still lacks an evaluation method that evaluates the multiple properties of these systems, all at once.As a potential solution, this paper proposes an evaluation methodology that provides a multidimensional assessment of recommender systems.The proposed method, which we call ComPer, combines the most common evaluation dimensions into a single, yet, general evaluation metric.ComPer is inspired by the idea that a recommender system mimics human beings; hence, it can be seen as a human and its outputs can be assessed as human's outputs.Up to our knowledge, this is the first evaluation approach that deals with recommenders as humans.ComPer aims to be thorough (by combining multiple dimensions), simple (by presenting the final result as a single value), and independent (by providing setting-independent results).The applicability of the proposed methodology is evaluated empirically using three different datasets.The initial results are promising in the sense that ComPer is able to give comparable results regardless of the experimental settings.
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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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".