A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search-Reference-Cited by-同舟云学术

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A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search

Published:2024-07-10 Issue: Volume:20 Page:2261-2265
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
language:
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Author:

Vecchiato Thomas¹^ORCID,Lucchese Claudio¹^ORCID,Nardini Franco Maria²^ORCID,Bruch Sebastian³^ORCID

Affiliation:

1. Ca' Foscari University of Venice, Venice, Italy

2. ISTI-CNR, Pisa, Italy

3. Pinecone, New York, NY, USA, MA, USA

Funder

HORIZON EUROPE Framework Programme

European Commission

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657931

Reference35 articles.

1. Alex Auvolat Sarath Chandar Pascal Vincent Hugo Larochelle and Yoshua Bengio. 2015. Clustering is Efficient for Approximate Maximum Inner Product Search. arxiv: 1507.05910 [cs.LG]

2. Multidimensional binary search trees used for associative searching

3. An Alternative Cross Entropy Loss for Learning-to-Rank

4. Sebastian Bruch. 2024. Foundations of Vector Retrieval. Springer Nature Switzerland.

5. Efficient and Effective Tree-based and Neural Learning to Rank

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