An Integrated-Model QoS-Based Graph for Web Service Recommendation
Bibliographic record
Abstract
Web services (WS) are integrated software components that facilitate interoperable machine-to-machine interaction over a network. In the era of Web 2.0, companies worldwide are actively deploying Web services within their business environments. As a result, designing effective Web service recommendation mechanisms based on Quality of Service (QoS) is attracting more attention. However, traditional Neighborhood-based Collaborative Filtering (CF) models fail to capture the actual relationships between users or services due to data sparsity. On the other hand, Random Walk (RW) algorithm, which has been categorized as a sparsity-tolerant recommendation approach, suffers from poor performance in terms of recommendation accuracy. In this paper, we aim at designing a recommendation model that achieves high recommendation accuracy over the transitional RW based model. First, we propose an Integrated-Model QoS-based Graph (IMQG), in which users and services represent the nodes while weighted QoS magnitudes and User/Service similarity measurements serve as the edges. We use Jaccard coefficient in several variants to separately compute similarities of both Users and Services. Then, Top-k Random Walk algorithm is applied to generate final recommendation list to active users. Finally, to demonstrate the effectiveness of our model, comprehensive experiments are conducted on a real-world QoS dataset. Analysis of the results shows high improvement in recommendation accuracy with more tolerance to data sparsity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".