Self-adaptive filtering using pid feedback controller in electronic commerce
Bibliographic record
Abstract
The performance of e-marketplaces plays a crucial role in attracting and retaining buyers. For example, a variation in the delivered Quality of Services (QoS) can frustrate buyers and they leave the e-marketplace, causing revenue loss. The inherent uncertainties of open marketplaces motivate the design of reputation systems to facilitate buyers in finding honest feedback from other buyers (advisers). Defining the threshold for acceptable level of honesty of advisers is very important, since inappropriately set thresholds would filter away possibly good advice, or the opposite - allow malicious buyers to badmouth good services. However, currently, there is no systematic approach for setting the honesty threshold. We propose a self-adaptive honesty threshold management mechanism based on PID feedback controller. Experimental results show that adaptively tuning the honesty threshold to the market performance enables honest buyers to obtain higher quality of services in comparison with static threshold values defined by intuition and used in previous work.
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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".