TGQ-Former uses metadata-guided hybrid queries and dual-gated modulation to improve visual token selection in multimodal e-commerce retrieval, raising average Hit Rate@100 by 6.04% over baselines.
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Intermediate decoder hidden states from frozen LVLMs fused with ID embeddings outperform caption representations and deliver state-of-the-art micro-video recommendation performance on two real-world benchmarks.
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Text-Guided Visual Representation Learning for Robust Multimodal E-Commerce Recommendation
TGQ-Former uses metadata-guided hybrid queries and dual-gated modulation to improve visual token selection in multimodal e-commerce retrieval, raising average Hit Rate@100 by 6.04% over baselines.
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Frozen LVLMs for Micro-Video Recommendation: A Systematic Study of Feature Extraction and Fusion
Intermediate decoder hidden states from frozen LVLMs fused with ID embeddings outperform caption representations and deliver state-of-the-art micro-video recommendation performance on two real-world benchmarks.