VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation
#VLM2Rec #modality collapse #vision-language model #multimodal recommendation #sequential recommendation #embedding #AI #machine learning
📌 Key Takeaways
- VLM2Rec addresses modality collapse in vision-language models for recommendations.
- It improves embedding quality for multimodal sequential recommendation tasks.
- The method enhances the integration of visual and textual data.
- It aims to boost recommendation accuracy by resolving embedding issues.
📖 Full Retelling
arXiv:2603.17450v1 Announce Type: cross
Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations. Inspired by the recent success of Large Language Models (LLMs) as high-capacity embedders, we investigate the use of Vision-Language Models (VLMs) as CF-aware multimodal encoders for SR. However, we find t
🏷️ Themes
AI Research, Recommendation Systems
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Original Source
arXiv:2603.17450v1 Announce Type: cross
Abstract: Sequential Recommendation (SR) in multimodal settings typically relies on small frozen pretrained encoders, which limits semantic capacity and prevents Collaborative Filtering (CF) signals from being fully integrated into item representations. Inspired by the recent success of Large Language Models (LLMs) as high-capacity embedders, we investigate the use of Vision-Language Models (VLMs) as CF-aware multimodal encoders for SR. However, we find t
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