VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend not only on how modalities are fused, but also on whether item content is represented in a semantic space aligned with preference matching. This issue is particularly important because raw visual features often preserve appearance similarity, while user decisions are typically driven by higher-level semantic factors such as style, material, and usage context. Motivated by this observation, we propose LVLM-grounded Multimodal Semantic Representation for Recommendation (VLM4...
| Authors | Ty Valencia, Burak Barlas, Varun Singhal et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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