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Random walks on the click graph

Published: 23 July 2007 Publication History

Abstract

Search engines can record which documents were clicked for which query, and use these query-document pairs as "soft" relevance judgments. However, compared to the true judgments, click logs give noisy and sparse relevance information. We apply a Markov random walk model to a large click log, producing a probabilistic ranking of documents for a given query. A key advantage of the model is its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively. We conduct experiments on click logs from image search, comparing our ("backward") random walk model to a different ("forward") random walk, varying parameters such as walk length and self-transition probability. The most effective combination is a long backward walk with high self-transition probability.

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cover image ACM Conferences
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
July 2007
946 pages
ISBN:9781595935977
DOI:10.1145/1277741
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: 23 July 2007

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Author Tags

  1. click data
  2. image search
  3. models
  4. user behavior
  5. web search

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SIGIR07
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SIGIR07: The 30th Annual International SIGIR Conference
July 23 - 27, 2007
Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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  • (2024)Revisiting Document Expansion and Filtering for Effective First-Stage RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657850(186-196)Online publication date: 10-Jul-2024
  • (2024)Measuring vaccination coverage and concerns of vaccine holdouts from web search logsNature Communications10.1038/s41467-024-50614-415:1Online publication date: 1-Aug-2024
  • (2024)Effective Adhoc Retrieval Through Traversal of a Query-Document GraphAdvances in Information Retrieval10.1007/978-3-031-56063-7_6(89-104)Online publication date: 23-Mar-2024
  • (2023)Less reliable media drive interest in anti-vaccine informationHarvard Kennedy School Misinformation Review10.37016/mr-2020-116Online publication date: 6-Jun-2023
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  • (2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
  • (2023)NosWalker: A Decoupled Architecture for Out-of-Core Random Walk ProcessingProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3582016.3582025(466-482)Online publication date: 25-Mar-2023
  • (2023)The Evolution of Web Search User Interfaces - An Archaeological Analysis of Google Search Engine Result PagesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578320(55-68)Online publication date: 19-Mar-2023
  • (2023)From 10 Blue Links Pages to Feature-Full Search Engine Results Pages - Analysis of the Temporal Evolution of SERP FeaturesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578307(338-345)Online publication date: 19-Mar-2023
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