MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels

Chen, Q; Geng, X; Rosset, C; Buractaon, C; Lu, J; Shen, T; Zhou, K; Xiong, C; Gong, Y; Bennett, P; Craswell, N; Xie, X; Yang, F; Tower, B; Rao, N; Dong, A; Jiang, W; Liu, Z; Li, M; Liu, C; Li, Z; Majumder, R; Neville, J; Oakley, A; Risvik, KM; Simhadri, HV; Varma, M; Wang, Y; Yang, L; Yang, M; Zhang, C

MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels

Chen, Q Geng, X Rosset, C Buractaon, C Lu, J Shen, T Zhou, K Xiong, C Gong, Y Bennett, P Craswell, N Xie, X Yang, F Tower, B Rao, N Dong, A Jiang, W Liu, Z Li, M Liu, C Li, Z Majumder, R Neville, J Oakley, A Risvik, KM Simhadri, HV Varma, M Wang, Y Yang, L Yang, M Zhang, C

Permalink

Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: WWW 2024 Companion - Companion Proceedings of the ACM Web Conference, 2024, pp. 292-301
Issue Date:: 2024-05-13

Recently Added

	Filename	Description	Size
	3589335.3648327.pdf	Published version	3.13 MB		View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, Q
dc.contributor.author	Geng, X
dc.contributor.author	Rosset, C
dc.contributor.author	Buractaon, C
dc.contributor.author	Lu, J
dc.contributor.author	Shen, T
dc.contributor.author	Zhou, K
dc.contributor.author	Xiong, C
dc.contributor.author	Gong, Y
dc.contributor.author	Bennett, P
dc.contributor.author	Craswell, N
dc.contributor.author	Xie, X
dc.contributor.author	Yang, F
dc.contributor.author	Tower, B
dc.contributor.author	Rao, N
dc.contributor.author	Dong, A
dc.contributor.author	Jiang, W
dc.contributor.author	Liu, Z
dc.contributor.author	Li, M
dc.contributor.author	Liu, C
dc.contributor.author	Li, Z
dc.contributor.author	Majumder, R
dc.contributor.author	Neville, J
dc.contributor.author	Oakley, A
dc.contributor.author	Risvik, KM
dc.contributor.author	Simhadri, HV
dc.contributor.author	Varma, M
dc.contributor.author	Wang, Y
dc.contributor.author	Yang, L
dc.contributor.author	Yang, M
dc.contributor.author	Zhang, C
dc.date	2024-05-13
dc.date.accessioned	2025-03-14T19:11:26Z
dc.date.available	2025-03-14T19:11:26Z
dc.date.issued	2024-05-13
dc.identifier.citation	WWW 2024 Companion - Companion Proceedings of the ACM Web Conference, 2024, pp. 292-301
dc.identifier.isbn	979-8-4007-0172-6
dc.identifier.uri	http://hdl.handle.net/10453/185822
dc.description.abstract	Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demands innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://github.com/microsoft/MSMARCO-Web-Search.
dc.language	en
dc.publisher	ACM
dc.relation.ispartof	WWW 2024 Companion - Companion Proceedings of the ACM Web Conference
dc.relation.ispartof	ACM Web Conference
dc.relation.isbasedon	10.1145/3589335.3648327
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
dc.type	Conference Proceeding
utslib.location.activity	Singapore
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2025-03-14T19:11:23Z
pubs.finish-date	2024-05-17
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2024-05-13
dc.location	USA

Abstract:

Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides rich information for various kinds of downstream tasks and encourages research in various areas, such as generic end-to-end neural indexer models, generic embedding models, and next generation information access system with large language models. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks that demands innovations in both machine learning and information retrieval system research domains. As the first dataset that meets large, real and rich data requirements, MS MARCO Web Search paves the way for future advancements in AI and system research. MS MARCO Web Search dataset is available at: https://github.com/microsoft/MSMARCO-Web-Search.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/185822