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
Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a user's evaluation of the review quality.
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 | 0.000 |
| 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 it