TextBridgeGNN pre-trains a graph recommender across domains and uses text-similarity edges to move ID-based knowledge into a new domain, improving cross-domain, multi-domain, and zero-shot recommendations.
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RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.
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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains a graph recommender across domains and uses text-similarity edges to move ID-based knowledge into a new domain, improving cross-domain, multi-domain, and zero-shot recommendations.
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Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.