Recommending Energy-Efficient Data Mining Services with Data as Contextual Factors
Bibliographic record
Abstract
Energy-efficiency is considered an important aspect when consuming web services. For data mining services, input dataset can have an impact on the energy consumption level as well as other QoS values of a service. For instance, a service not good at dealing with high-dimensional data may consume more energy than another service offering the same functionality. Therefore, when recommending such services, it is beneficial to consider dataset information in the recommendation process. However, in the past research on service recommender systems, usually, only the historical data on how services are consumed by different users is taken as the main source for making recommendations. Sometimes, contextual factors such as time and location (when and where the services are consumed) are also included. In this paper, we propose to consider dataset information as contextual factors in the recommendation model. We apply contextual modeling approach to incorporate them into our service recommender system. We explore different ways of representing dataset information as contextual factors and investigate their impacts on recommendation accuracy. Experimental results show that our introduced approach outperforms the baseline model by 11.7-22.6% on various evaluation metrics measuring recommendation accuracy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".