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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".