When does Relevance Mean Usefulness and User Satisfaction in Web Search?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Relevance is a fundamental concept in information retrieval (IR) studies. It is however often observed that relevance as annotated by secondary assessors may not necessarily mean usefulness and satisfaction perceived by users. In this study, we confirm the difference by a laboratory study in which we collect relevance annotations by external assessors, usefulness and user satisfaction information by users, for a set of search tasks. We also find that a measure based on usefulness rather than relevance annotated has a better correlation with user satisfaction. However, we show that external assessors are capable of annotating usefulness when provided with more search context information. In addition, we also show that it is possible to generate automatically usefulness labels when some training data is available. Our findings explain why traditional system-centric evaluation metrics are not well aligned with user satisfaction and suggest that a usefulness-based evaluation method can be defined to better reflect the quality of search systems perceived by the users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 |
| 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 it