SF-UBM enables privacy-preserving cross-domain LLM recommendation by federating semantic item representations, distilling domain knowledge, and aligning preferences into LLM soft prompts.
A survey on cross-domain sequential recommendation
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MOSAIC decomposes user intent into three orthogonal components via a triple-encoder architecture with adversarial training and dynamic gating to outperform baselines in multi-domain session recommendations.
LGCD creates pseudo-overlapping user data via LLM reasoning and uses conditional diffusion to generate target-domain user representations for inter-domain sequential recommendation without real overlapping users.
LLM agents enable a shift in recommender systems from opaque hidden profiles to governable, inspectable, and portable user representations.
LLM-EDT improves cross-domain sequential recommendation by using LLMs for transferable item augmentation, dual-phase training to handle domain transitions, and domain-aware profiling to build user profiles.
AIR framework achieves ~400x faster LLM-based cross-domain recommendation via offline intent construction and online retrieval, with SOTA results on public data and +3.446% GMV lift in live A/B tests.
citing papers explorer
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Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation
SF-UBM enables privacy-preserving cross-domain LLM recommendation by federating semantic item representations, distilling domain knowledge, and aligning preferences into LLM soft prompts.
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MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations
MOSAIC decomposes user intent into three orthogonal components via a triple-encoder architecture with adversarial training and dynamic gating to outperform baselines in multi-domain session recommendations.
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From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
LGCD creates pseudo-overlapping user data via LLM reasoning and uses conditional diffusion to generate target-domain user representations for inter-domain sequential recommendation without real overlapping users.
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From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents
LLM agents enable a shift in recommender systems from opaque hidden profiles to governable, inspectable, and portable user representations.
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LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
LLM-EDT improves cross-domain sequential recommendation by using LLMs for transferable item augmentation, dual-phase training to handle domain transitions, and domain-aware profiling to build user profiles.
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Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
AIR framework achieves ~400x faster LLM-based cross-domain recommendation via offline intent construction and online retrieval, with SOTA results on public data and +3.446% GMV lift in live A/B tests.