Article

Time-based language models

Authors:

Xiaoyan Li,

W. Bruce CroftAuthors Info & Claims

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

Pages 469 - 475

https://doi.org/10.1145/956863.956951

Published: 03 November 2003 Publication History

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Abstract

We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

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Wang JJatowt AYoshikawa MCai YChen HDuh WHuang HKato MMothe JPoblete B(2023)BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal InformationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591686(812-821)Online publication date: 19-Jul-2023
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Index Terms

Time-based language models
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

Information

Published In

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

November 2003

592 pages

ISBN:1581137230

DOI:10.1145/956863

General Chair:
Donald Kraft
Louisiana State University
,
Program Chairs:
Ophir Frieder
Illinois Institute of Technology
,
Joachim Hammer
University of Florida
,
Sajda Qureshi
University of Nebraska, Omaha
,
Len Seligman
The MITRE Corporation

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2003

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CIKM03: 12th International Conference on Information and Knowledge Management

November 3 - 8, 2003

LA, New Orleans, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
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Wang YWang ZZhang HLiu Z(2023)Microblog Retrieval Based on Concept-Enhanced Pre-Training ModelACM Transactions on Knowledge Discovery from Data10.1145/355231117:3(1-32)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3552311
Wang JJatowt AYoshikawa MCai YChen HDuh WHuang HKato MMothe JPoblete B(2023)BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal InformationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591686(812-821)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591686
Jatowt ASato MDraxl SDuan YCampos RYoshikawa M(2023)Is this news article still relevant? Ranking by contemporary relevance in archival searchInternational Journal on Digital Libraries10.1007/s00799-023-00377-y25:2(197-216)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s00799-023-00377-y
Rizzo SBrucato MMontesi D(2022)Ranking Models for the Temporal Dimension of TextACM Transactions on Information Systems10.1145/356548141:2(1-34)Online publication date: 21-Dec-2022
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