Overview of the TREC 2023 Product Product Search Track

Cumulated gain-based evaluation of IR techniques

K. Järvelin and J. Kekäläinen · 2002

Earlier work this paper cites.

Estimating average precision with incomplete and imperfect judgments

Emine Yilmaz and Javed A. Aslam · 2006

Earlier work this paper cites.

Natural questions: A benchmark for question answering research

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc V. Le, and Slav Petrov · 2019

Earlier work this paper cites.

Trec deep learning track: Reusable test collections in the large data regime

Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M Voorhees, and Ian Soboroff · 2021

Earlier work this paper cites.

Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations

Jimmy J. Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira, and David R. Cheriton · 2021

Cited alongside, same era.

Inpars: Data augmentation for information retrieval using large language models

Original

Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira · 2022

Cited alongside, same era.

Tevatron: An efficient and flexible toolkit for dense retrieval

Original

Luyu Gao, Xueguang Ma, Jimmy J. Lin, and Jamie Callan · 2022

Cited alongside, same era.

Shopping queries dataset: A large-scale esci benchmark for improving product search

Original

Chandan K. Reddy, Lluís Màrquez i Villodre, Francisco B. Valero, Nikhil S. Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, and Karthik Subbian · 2022

Later among the works it cites.

Inpars-v2: Large language models as efficient dataset generators for information retrieval

Original

Vitor Jeronymo, Luiz Henrique Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto de Alencar Lotufo, Jakub Zavrel, and Rodrigo Nogueira · 2023

Closest in time.

Mteb: Massive text embedding benchmark, 2023

Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2023

Closest in time.