2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.IR2025
VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings
Ramin Giahi, Kehui Yao, Sriram Kollipara +6
Multimodal learning plays a critical role in e-commerce recommendation platforms today, enabling accurate recommendations and product understanding. However, existing vision-langua…
cs.IR2024★ 2 cited
Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations
Luyi Ma, Xiaohan Li, Zezhong Fan +7
Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources,…