Simulating a trust-based service recommender system for decentralised user modelling environment
Bibliographic record
Abstract
Trust and reputation mechanisms are often used in peer-to-peer networks, multi-agent systems and online communities to differentiate among members of the community as well as to recommend service providers. Although different users have different needs and expectations in different aspects of the service providers, few exiting trust models use differentiated trust values for judging different aspects of service providers. We have proposed a multi-aspect trust model where each user has two sets of trust values: 1) trust on different aspects of the quality of service providers; 2) differentiated trust on the recommendations provided by other users for each of these aspects. This trust model is used to recommend service providers in a decentralised user modelling system where agents have different preference weights in three different criteria of service providers. The paper focuses on the evaluation of the approach via a simulation on a large real social network. The results show that the trust model allows agents to learn from experience to find good service providers by using recommendations from their friends and that the model is robust with respect to colluding malicious agents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".