SemaCDR builds a unified semantic space with LLM-generated domain-agnostic features and adaptive fusion to improve cross-domain sequential recommendations over baselines.
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DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.
LLM-driven personalization of CS1 RegEx worksheets based on learner profiles raises completion to over 99% and boosts correctness by 18.2% for at-risk students while preserving perceived difficulty.
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.
SpatialBalancing is a system that turns revision trade-offs into spatial navigation so writers can iteratively balance scientific exposition and narrative engagement with LLM assistance.
citing papers explorer
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SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation
SemaCDR builds a unified semantic space with LLM-generated domain-agnostic features and adaptive fusion to improve cross-domain sequential recommendations over baselines.
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DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.
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Beyond One-Size-Fits-All Exercises: Personalizing Computer Science Worksheets with Large Language Models
LLM-driven personalization of CS1 RegEx worksheets based on learner profiles raises completion to over 99% and boosts correctness by 18.2% for at-risk students while preserving perceived difficulty.
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Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook
The survey organizes foundation models for sensor-based HAR into a lifecycle taxonomy and identifies three trajectories: HAR-specific models from scratch, adaptation of general time-series models, and integration with large language models.
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Spatial Balancing: Harnessing Spatial Reasoning to Balance Scientific Exposition and Narrative Engagement in LLM-assisted Science Communication Writing
SpatialBalancing is a system that turns revision trade-offs into spatial navigation so writers can iteratively balance scientific exposition and narrative engagement with LLM assistance.