Browsing and keyword-based profiles: a cautionary tale
Bibliographic record
Abstract
In the research presented, adaptive user profiles are used to rate Web pages with respect to possible user interest. The user profiles consist of weighted keywords and the adaptation is based on the Hebbian Learning Model with direct user feedback. A user study was conducted to determine if the system would "learn" over a series of five sessions when there was no explicit task other than to browse. The results are analogous to reading the news, i.e., it is not possible to predict what pages a user will read if there is no explicit task. We suggest that a shift away from content to document style (genre) and other user characteristics may be more effective.
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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 | 0.041 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.012 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".