Decoupling Location and Preference: A Dual-Branch Architecture for Robust QoS Prediction Under Extreme Sparsity
Bibliographic record
Abstract
Quality of Service (QoS) prediction faces challenges from location-dependent variability and sparse user-service interactions. Existing methods often struggle to integrate location information (e.g., using fixed weights for spatial attributes) or learn representative features from sparse matrices. This paper proposes a method for Decoupling Location and Preference via a dual-branch architecture for robust QoS prediction under extreme sparsity, called DLP. It integrates location and preference features to address the challenges of sparsity and contextual variability. Unlike conventional single-stream or simple concatenation methods, DLP features a novel dual-branch architecture that decouples heterogeneous features and specializes in processing them: Location context and user-service preferences. The first branch, a location feature extraction network, processes user and service geographical and network information. It utilizes an attention mechanism to dynamically weight spatial attributes (instead of fixed weights) based on their actual impact on QoS and selects the most salient co-location features to model spatial interactions. The second branch, a preference feature extraction network, constructs high-dimensional feature representations from similarity-based user-service vectors derived from the sparse QoS matrix. It employs a multi-layer feature extraction block that hierarchically aggregates intermediate features to compensate for information loss during transformation, thereby capturing richer user/service preferences. Finally, a feature fusion prediction network integrates the learned location and preference features to generate accurate QoS predictions. Ablation studies and analysis validate that each component contributes significantly to performance gains. Extensive experiments on the WS-DREAM dataset show that DLP outperforms 22 baselines across 2.5%–20% sparsity, excelling in throughput prediction (achieving reductions up to 9.07% in Mean Absolute Error and 28.86% in Root Mean Squared Error at 2.5% sparsity) and validating its superior QoS prediction 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".