Federated Deep Recommendation System Based on Multi-View Feature Embedding
Bibliographic record
Abstract
The application of recommendation systems online services is becoming more and more extensive. However, most existing recommendation algorithms centralize multi-party information into a central processor, which may lead to the risk of privacy leakage. And many enterprises or institutions still have the problem that data cannot be shared. Federated learning has been introduced into recommendation algorithms for privacy- aware distributed learning. A typical federated learning is that each client uses local data to train a shared model, the server uses their gradient information to form a global model, and then each client updates. In this paper, we propose a federated deep recommendation algorithm called FedHe-mlp that applies a federated deep learning for data privacy protection, and combines heterogeneous information network (HIN) and matrix factorization technique for better prediction performance. First, each client obtains heterogeneous information through meta- paths, then we combine matrix factorization and heterogeneous information to mine the latent features and heterogeneous features of each client. Finally, We propose a deep neural network that considers features from multiple views. Extensive experiments on three public datasets demonstrate that FedHe- mlp can provide excellent convergence speed, recommendation accuracy, and communication efficiency while preserving data privacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.020 | 0.025 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".