LLM agents enable users to integrate cross-platform and offline data for personalization that outperforms single-platform baselines in proof-of-concept tests.
User modeling and user profiling: A comprehensive survey
4 Pith papers cite this work. Polarity classification is still indexing.
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TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
Humanities scholars require recommender user models for digital archives that account for context volatility, epistemic trust, contrastive seeking, and strand continuity instead of stable preferences and session-bounded interactions.
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.
citing papers explorer
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LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries
LLM agents enable users to integrate cross-platform and offline data for personalization that outperforms single-platform baselines in proof-of-concept tests.
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TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
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What Do Humanities Scholars Need? A User Model for Recommendation in Digital Archives
Humanities scholars require recommender user models for digital archives that account for context volatility, epistemic trust, contrastive seeking, and strand continuity instead of stable preferences and session-bounded interactions.
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A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
A survey of personalization techniques and foundation model adaptations in federated settings for privacy-preserving recommendations, emphasizing their architectural intersection.