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20122026
most citedDocument Clustering Evaluation: Divergence from a Random Baseline

20 citations · 37 across the 4 of their papers we have counts for

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cs.IR2025

Information Retrieval for Climate Impact

Maarten de Rijke, Bart van den Hurk, Flora Salim +28

The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and di…

cs.IR2021★ 17 cited

Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation

Joel Mackenzie, Andrew Trotman, Jimmy Lin

Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniqu…

cs.IR2020

Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format

Jimmy Lin, Joel Mackenzie, Chris Kamphuis +5

There exists a natural tension between encouraging a diverse ecosystem of open-source search engines and supporting fair, replicable comparisons across those systems. To balance th…

cs.IR2019

Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

Jon Degenhardt, Surya Kallumadi, Utkarsh Porwal +1

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a pl…

cs.IR2012★ 20 cited

Document Clustering Evaluation: Divergence from a Random Baseline

Christopher M. De Vries, Shlomo Geva, Andrew Trotman

Divergence from a random baseline is a technique for the evaluation of document clustering. It ensures cluster quality measures are performing work that prevents ineffective cluste…