Goal Modeling-based Evaluation for Personalized Recommendation Systems
Bibliographic record
Abstract
Designing and evaluating a recommendation algorithm are typically user-centric operations. However, users are not the sole party of real-world applications. Therefore, designing, deploying, and evaluating a personalized system should consider all parties (or stakeholders). Dealing with recommender systems as multistakeholders systems is a relatively new research direction. In particular, considering the requirements of multiple stakeholders in selecting the best algorithm among a set of alternatives has not been discussed extensively. An adaptive evaluation approach that can handle personalized needs from all parties is therefore required. This paper aims to fill this gap by introducing the use of goal modeling to support the selection of a recommendation algorithm. Through an illustrative example, we show the feasibility of modeling the recommendation alternatives and their contributions to multiple stakeholders’ goals so that the selected algorithm is well-aligned with the overall system requirements. Accordingly, we say that the goal modeling approach has the potential of helping practitioners and researchers to better reason about algorithms selection and, therefore, advances the development 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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".