SPACE: Self-Supervised Dual Preference Enhancing Network for Multimodal Recommendation
Bibliographic record
Abstract
Multimodal recommendation is an emerging task with the goal of improving the effectiveness of the recommendation system by utilizing multimodal data (images, texts, etc.). Most previous methods have struggled with the ability to mine item semantic relationships while guaranteeing accurate modeling of user modality preferences, resulting in low recommendation accuracy. To address this issue, this paper proposes a novel and effective Self-suPervised duAl preference enhanCing nEtwork for multimodal recommendation, named SPACE, which further mines user preferences towards historical interactions and multimodal features of items to obtain more precise user and item representation. Specifically, we design an interaction preference enhancing module to learn both interactive and latent semantic relationships between users and items. Then, a modality preference enhancing module is established by introducing self-supervised learning (SSL), which aims to strengthen the role of dominant modality-specific representation of items. Finally, the enhanced interaction and modality representations are fused, and the recommendation performance is largely improved by utilizing dual joint prediction. Extensive experiments are conducted on three real-world datasets, and the simulation results demonstrate that the proposed SPACE model outperforms the state-of-the-art multimodal recommendation 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 | 0.003 |
| 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 | 0.002 |
| 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".