Synapse combines two-phase dense retrieval with LLM-guided differential evolution to optimize job-candidate alignment, reporting 22% nDCG@10 gains and over 60% relative score improvements.
theory of mind
7 Pith papers cite this work, alongside 1,387 external citations. Polarity classification is still indexing.
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A framework distills noisy user logs into rules and preference pairs, clusters them by query and feedback, then fine-tunes either an expert adapter or a critic adapter to improve future responses.
Mind modeling grounds personalization in explicit, revisable attribution of users' mental states via Theory of Mind, enabling more interpretable adaptive systems than traditional behavior-based approaches.
A weighted similarity ensemble unifies user-item and item-item collaborative filtering using shared embeddings to deliver competitive top-N recommendations without extra fine-tuning.
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
A multi-agent multimodal system with fact-grounded adjudication and a dynamic two-tier preference graph cuts false positives in content filtering by 74.3% and nearly doubles F1-score versus text-only baselines while supporting user-driven Delta adjustments.
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.
citing papers explorer
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Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Introduces semantic Pareto-DQN for multi-objective recommendation that sustains trajectory variance to improve diversity and fairness on MovieLens with limited engagement loss.