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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 7376–7400 of 9051 papers

TitleStatusHype
Personalize Anything for Free with Diffusion Transformer—0
Personalized Bundle List Recommendation—0
Personalized Elastic Embedding Learning for On-Device Recommendation—0
Personalized Game Difficulty Prediction Using Factorization Machines—0
Personalized neural language models for real-world query auto completion—0
Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling—0
Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters—0
Personalized Query Rewriting in Conversational AI Agents—0
Personalized Re-ranking for Improving Diversity in Live Recommender Systems—0
Personalized Student Attribute Inference—0
Personalizing Fairness-aware Re-ranking—0
Personal Names in Modern Turkey—0
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation—0
PersonificationNet: Making customized subject act like a person—0
Person Re-Identification via Active Hard Sample Mining—0
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction—0
Perturbation-based Active Learning for Question Answering—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Perturbation Diversity Certificates Robust Generalisation—0
PetKaz at SemEval-2024 Task 8: Can Linguistics Capture the Specifics of LLM-generated Text?—0
Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities—0
PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition—0
Phasic Diversity Optimization for Population-Based Reinforcement Learning—0
Phenomenology and dynamics of competitive ecosystems beyond the niche-neutral regimes—0
Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science—0
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