research-article

Search result diversity for informational queries

Authors:

Michael J. Welch,

Christopher OlstonAuthors Info & Claims

WWW '11: Proceedings of the 20th international conference on World wide web

Pages 237 - 246

https://doi.org/10.1145/1963405.1963441

Published: 28 March 2011 Publication History

Abstract

Ambiguous queries constitute a significant fraction of search instances and pose real challenges to web search engines. With current approaches the top results for these queries tend to be homogeneous, making it difficult for users interested in less popular aspects to find relevant documents. While existing research in search diversification offers several solutions for introducing variety into the results, the majority of such work is predicated, implicitly or otherwise, on the assumption that a single relevant document will fulfill a user's information need, making them inadequate for many informational queries. In this paper we present a search-diversification algorithm particularly suitable for informational queries by explicitly modeling that the user may need more than one page to satisfy their need. This modeling enables our algorithm to make a well-informed tradeoff between a user's desire for multiple relevant documents, probabilistic information about an average user's interest in the subtopics of a multifaceted query, and uncertainty in classifying documents into those subtopics. We evaluate the effectiveness of our algorithm against commercial search engine results and other modern ranking strategies, demonstrating notable improvement in multiple document scenarios.

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Cited By

Patro WNiaz APrasath R(2022)Comprehensive Information Retrieval Using Fine-Tuned Bert Model and Topic-Assisted Query ExpansionAmbient Intelligence in Health Care10.1007/978-981-19-6068-0_12(117-132)Online publication date: 23-Nov-2022
https://doi.org/10.1007/978-981-19-6068-0_12
Castells PHurley NVargas S(2021)Novelty and Diversity in Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_16(603-646)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_16
Dumitrescu ASantini S(2021)Full coverage of a reader's interests in context‐based information filteringJournal of the Association for Information Science and Technology10.1002/asi.2447072:8(1011-1027)Online publication date: 5-Jul-2021
https://dl.acm.org/doi/10.1002/asi.24470
Show More Cited By

Index Terms

Search result diversity for informational queries
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Other conferences

WWW '11: Proceedings of the 20th international conference on World wide web

March 2011

840 pages

ISBN:9781450306324

DOI:10.1145/1963405

General Chairs:
S. Sadagopan
IIIT-Bangalore, India
,
Krithi Ramamritham
IIT-Bombay, India
,
Arun Kumar
IBM Research, India
,
M. P. Ravindra
Infosys E & R, India
,
Program Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Kumar
Yahoo! Research, USA

Copyright © 2011 ACM.

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
The International Institute of Information Technology Bangalore: The International Institute of Information Technology Bangalore

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 March 2011

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WWW '11

WWW '11: 20th International World Wide Web Conference

March 28 - April 1, 2011

Hyderabad, India

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Cited By

Patro WNiaz APrasath R(2022)Comprehensive Information Retrieval Using Fine-Tuned Bert Model and Topic-Assisted Query ExpansionAmbient Intelligence in Health Care10.1007/978-981-19-6068-0_12(117-132)Online publication date: 23-Nov-2022
https://doi.org/10.1007/978-981-19-6068-0_12
Castells PHurley NVargas S(2021)Novelty and Diversity in Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_16(603-646)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_16
Dumitrescu ASantini S(2021)Full coverage of a reader's interests in context‐based information filteringJournal of the Association for Information Science and Technology10.1002/asi.2447072:8(1011-1027)Online publication date: 5-Jul-2021
https://dl.acm.org/doi/10.1002/asi.24470
Li JCai TDeng KWang XSellis TXia F(2020)Community-diversified influence maximization in social networksInformation Systems10.1016/j.is.2020.10152292(101522)Online publication date: Sep-2020
https://doi.org/10.1016/j.is.2020.101522
Setlur VTory MDjalali AFu WPan SBrdiczka OChau PCalvary G(2019)Inferencing underspecified natural language utterances in visual analysisProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3302270(40-51)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3302270
Kulshrestha JEslami MMessias JZafar MGhosh SGummadi KKarahalios K(2019)Search bias quantificationInformation Retrieval10.1007/s10791-018-9341-222:1-2(188-227)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10791-018-9341-2
Puschmann C(2018)Beyond the Bubble: Assessing the Diversity of Political Search ResultsDigital Journalism10.1080/21670811.2018.1539626(1-20)Online publication date: 28-Nov-2018
https://doi.org/10.1080/21670811.2018.1539626
Skowron PLackner MBrill MPeters DElkind E(2017)Proportional rankingsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171642.3171701(409-415)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171642.3171701
Kulshrestha JEslami MMessias JZafar MGhosh SGummadi KKarahalios KLee CPoltrock SBarkhuus LBorges MKellogg W(2017)Quantifying Search BiasProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing10.1145/2998181.2998321(417-432)Online publication date: 25-Feb-2017
https://dl.acm.org/doi/10.1145/2998181.2998321
Yu BFang MTao D(2016)Per-round knapsack-constrained linear submodular banditsNeural Computation10.1162/NECO_a_0088728:12(2757-2789)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1162/NECO_a_00887
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