SOTAVerified

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

Showing 49014910 of 9051 papers

TitleStatusHype
Recent Advances in Diversified Recommendation0
Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark0
Channel State Acquisition in Uplink NOMA for Cellular-Connected UAV: Exploitation of Doppler and Modulation Diversities0
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution0
Recognition of non-domain phrases in automatically extracted lists of terms0
Recognizing long-form speech using streaming end-to-end models0
Recommendation Systems with Distribution-Free Reliability Guarantees0
Recommenders with a mission: assessing diversity in newsrecommendations0
Recommending Accurate and Diverse Items Using Bilateral Branch Network0
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