CoPo: Self-supervised Contrastive Learning for Popularity Prediction in MEC Networks
Bibliographic record
Abstract
Mobile Edge Caching (MEC) technology aims to provide high-quality multimedia content to mobile users by bringing storage and computation resources closer to the edge of the network. MEC networks, however, face several challenges such as limited storage capacity, dynamic network conditions, and the need for low-latency content delivery. To address these challenges, recent research has focused on integrating MEC networks with Deep Neural Networks (DNNs), in particular, supervised learning models. One significant limitation of supervised popularity prediction models is the requirement for manual labeling of contents as popular or unpopular by investigating users’ past behavior, which can be a time-intensive task. This paper proposes a self-supervised learning algorithm called Contrastive learning Popularity (CoPo) prediction framework to predict the dynamic content popularity in a MEC network. The framework utilizes the distinguishing aspect of the Contrastive Learning (CL) paradigm to recognize differences among input samples, including users’ contextual information and is based on the Long Short Term Memory (LSTM) model to capture temporal information. Simulation results illustrate that the proposed CoPo framework outperforms the self-supervised/unsupervised state-of-the-art methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".