Multimodal Federated Learning for Personalized Clothing Recommendation
Bibliographic record
Abstract
Federated clothing recommendation suggests clothing items to customers (users) based on their past purchase behaviors in a privacy-preserving manner. Current federated recommendation systems face two main challenges. The first is limited information exploration. Existing methods mainly rely on item ID-based embedding and ignore multimodal information about items. The second challenge, data heterogeneity, arising from that users perceive the same clothing item differently, leads to performance degradation. We therefore propose a multimodal federated learning framework for personalized clothing recommendation (MMFashion). To address the first challenge, we represent each item using its image and text descriptions, generating rich item representations from which user preferences can be derived. To address the second challenge, we introduce personalized image and text adapters to guide the local model in capturing item attributes that align with user’s interests. Such user-specific item representations effectively capture individual preferences. We also introduce a threshold-based regularization loss to push the learned embeddings of user preferences and dislikes apart. To evaluate the effectiveness of MMFashion, we construct datasets by extracting two clothing types (i.e., long sleeves and outerwear) from a real-world clothing dataset released by a fashion retail company. Experimental results show that MMFashion consistently outperforms existing methods, supporting its effectiveness in addressing the challenges in federated clothing recommendation.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".