Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations

• URL: http://arxiv.org/abs/2502.15542v1
• Date: Fri, 21 Feb 2025 15:50:14 GMT
• Title: Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations
• Authors: Wenyu Zhang, Jie Luo, Xinming Zhang, Yuan Fang,
• Abstract summary: textbfPTMRec is a novel framework that bridges the domain gap between pre-trained models and recommendation systems.<n>This framework not only eliminates the need for costly additional pre-training but also flexibly accommodates various parameter-efficient tuning methods.
• Score: 12.79899622986449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the explosive growth of multimodal content online, pre-trained visual-language models have shown great potential for multimodal recommendation. However, while these models achieve decent performance when applied in a frozen manner, surprisingly, due to significant domain gaps (e.g., feature distribution discrepancy and task objective misalignment) between pre-training and personalized recommendation, adopting a joint training approach instead leads to performance worse than baseline. Existing approaches either rely on simple feature extraction or require computationally expensive full model fine-tuning, struggling to balance effectiveness and efficiency. To tackle these challenges, we propose \textbf{P}arameter-efficient \textbf{T}uning for \textbf{M}ultimodal \textbf{Rec}ommendation (\textbf{PTMRec}), a novel framework that bridges the domain gap between pre-trained models and recommendation systems through a knowledge-guided dual-stage parameter-efficient training strategy. This framework not only eliminates the need for costly additional pre-training but also flexibly accommodates various parameter-efficient tuning methods.

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