tutorial

Interactive Information Retrieval: Models, Algorithms, and Evaluation

Author:

Chengxiang ZhaiAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2662 - 2665

https://doi.org/10.1145/3404835.3462811

Published: 11 July 2021 Publication History

Abstract

Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR), where we would attempt to model and optimize an entire interactive retrieval process (rather than a single query) with consideration of many different ways a user can potentially interact with a search engine. This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR, ending with a brief discussion of the major open challenges in IIR and some of the most promising future research directions.

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Cited By

Kurland OTennenholtz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532771
Wang HJia YWang HDiaz FShah CSuel TCastells PJones RSakai T(2021)Interactive Information Retrieval with Bandit FeedbackProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462810(2658-2661)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462810

Index Terms

Interactive Information Retrieval: Models, Algorithms, and Evaluation
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

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Cited By

Kurland OTennenholtz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532771
Wang HJia YWang HDiaz FShah CSuel TCastells PJones RSakai T(2021)Interactive Information Retrieval with Bandit FeedbackProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462810(2658-2661)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462810

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