Real‐time helpfulness prediction based on voter opinions
Bibliographic record
Abstract
SUMMARY This paper studies the problem of designing real‐time helpfulness prediction algorithms. Instead of following the conventional route, in which the fraction of positive votes is used as the measure of helpfulness, we give ‘helpfulness’ a naturally sensible and mathematically precise definition, namely, as the probability that a user will vote ‘helpful’ on the user‐generated content. Building on this definition, we introduce a principled methodology to helpfulness prediction, in which the prediction problem is naturally formulated as an optimization problem. Under this proposed methodology, we first develop a batch (off‐line) algorithm. Experiments on data from Amazon.com suggest that our proposed model in fact outperforms the previously reported prediction algorithm, support vector regression. In some circumstances, an online algorithm that can update the model as additional data arrive is required. In light of this, we proposed an online algorithm that incrementally updates the parameters of the model. Finally, an efficient hybrid algorithm is provided to increase the convergence rate and prediction precision. The final two algorithms are tested on real‐life user‐generated contents, and experimental results illustrate that the hybrid approach efficiently processes incoming data and generates reliable helpfulness predictions for users. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| 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".