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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 7101–7125 of 9051 papers

TitleStatusHype
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation—0
Rethinking the Evaluation of Unbiased Scene Graph Generation—0
UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems—0
User Clustering for Rate Splitting using Machine Learning—0
AAMDM: Accelerated Auto-regressive Motion Diffusion Model—0
Rethinking Video Salient Object Ranking—0
Retinal Vessel Segmentation via Neuron Programming—0
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe—0
Retrievability in an Integrated Retrieval System: An Extended Study—0
A Nature-Inspired Colony of Artificial Intelligence System with Fast, Detailed, and Organized Learner Agents for Enhancing Diversity and Quality—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Retrieval & Fine-Tuning for In-Context Tabular Models—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Counterfactual Generation for QA—0
Retrieval-guided Cross-view Image Synthesis—0
RetriBooru: Leakage-Free Retrieval of Conditions from Reference Images for Subject-Driven Generation—0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
Retrieving Implicit and Explicit Emotional Events Using Large Language Models—0
A Natural Diet: Towards Improving Naturalness of Machine Translation Output—0
A Natural Diet: Towards Improving Naturalness of Machine Translation Output—0
Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students—0
User-Creator Feature Polarization in Recommender Systems with Dual Influence—0
ReverseORC: Reverse Engineering of Resizable User Interface Layouts with OR-Constraints—0
Review of Clustering-Based Recommender Systems—0
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