Agent4POI generates context-conditioned multimodal affordance representations via a four-phase LLM agent, achieving 23.2% relative gains over baselines on POI benchmarks with reduced degradation under context shifts.
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A new framework integrating deep interest mining, cross-modal semantic alignment, and quality-aware reinforcement learning generates higher-quality Semantic IDs and outperforms prior methods on recommendation benchmarks.
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Agent4POI: Agentic Context-Conditioned Affordance Reasoning for Multimodal Point-of-Interest Recommendation
Agent4POI generates context-conditioned multimodal affordance representations via a four-phase LLM agent, achieving 23.2% relative gains over baselines on POI benchmarks with reduced degradation under context shifts.
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Deep Interest Mining with Cross-Modal Alignment for SemanticID Generation in Generative Recommendation
A new framework integrating deep interest mining, cross-modal semantic alignment, and quality-aware reinforcement learning generates higher-quality Semantic IDs and outperforms prior methods on recommendation benchmarks.