Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews
Bibliographic record
Abstract
With the fast increase of online services of all kinds, users start to care more about the Quality of Service (QoS) that a service provider can offer besides the functionalities of the services. As a result, QoS-based service selection and recommendation have received significant attention since the mid-2000s. However, existing approaches primarily consider a small number of standard QoS parameters, most of which relate to the response time, fee, availability of services, and so on. As online services start to diversify significantly over different domains, these small set of QoS parameters will not be able to capture the different quality aspects that users truly care about over different domains. Most existing approaches for QoS data collection depend on the information from service providers, which are sensitive to the trustworthiness of the providers. Some service monitoring mechanisms collect QoS data through actual service invocations but may be affected by actual hardware/software configurations. In either case, domain-specific QoS data that capture what users truly care about have not been successfully collected or analyzed by existing works in service computing. To address this demanding issue, we develop a statistical learning approach to extract domain-specific QoS features from user-provided service reviews. In particular, we aim to classify user reviews based on their sentiment orientations into either a positive or negative category. Meanwhile, statistical feature selection is performed to identify statistically nontrivial terms from review text, which can serve as candidate QoS features. We also develop a topic models-based approach that automatically groups relevant terms and returns the term groups to users, where each term group corresponds to one high-level quality aspect of services. We have conducted extensive experiments on three real-world datasets to demonstrates the effectiveness of our approach.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".