Research on Cloud Service Provider Recommendation Model based on User Preference
Bibliographic record
Abstract
Based on the rise and wide application of cloud services, this paper proposes and implements a cloud service provider recommendation model based on user needs and preferences.The model consists of three parts.First, based on the needs of users, determine the subjective dimensions of users' demand for cloud services, and realize the measurement of user preferences.Secondly, according to the service capability of the cloud service provider, the ability of the cloud service provider to meet the needs of users is measured.From the perspective of cloud service providers, after determining the indicators that can reflect the service capabilities of cloud service providers, innovatively establish a bridge between service capabilities and user needs.Realize the evaluation of cloud service providers from the perspective of demand realization, that is, to measure their demand satisfaction ability.Finally, according to the recommendation rules in this article, the similarity distance between the user and the candidate cloud service provider based on requirements is compared, and the cloud service provider that matches the user's corresponding needs and preferences is recommended to the user, and personalized decision-making recommendation for the cloud service provider is realized.The recommendation system proposed and implemented in this paper is no longer limited to the evaluation of cloud service providers, but in the recommendation process, it combines the needs and preferences of cloud service users and specific cloud service field characteristics and other information and combines fuzzy evaluation methods.And similar distance and other theories, to give users more satisfactory recommendations, and make personalized recommendations for users.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".