On the Social Aspects of Personalized Ranking for Web Services
Bibliographic record
Abstract
Ranking is an important step once automated discovery in Web Services is enabled, it allows for automated selection of the best matched service, out of the discovered ones. However, automated selection of the best matched service is not as simple as it may look like. Different service consumers may have different preferences to select the service providers, which may even depend upon their past interactions. Various approaches have been proposed that allow ranking of services based on different functional and non-functional aspects. However, we believe that the selection of services based on the analysis of the past interactions of service consumers or their social-network could be another effective way to rank the services for the benefit of service consumers. In this paper, we present a community-aware personalized approach for recommending and ranking Web Services for a service consumer. It is based on analysis of historical interactions among service consumers and service providers. We perform analysis and mining on the log information of service consumers and service providers, model their past interactions as social network, apply standard social-network analysis techniques, and use this information in ranking Web Services.
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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 | 0.000 |
| Open science | 0.001 | 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".