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Incorporating post-click behaviors into a click model

Published: 19 July 2010 Publication History

Abstract

Much work has attempted to model a user's click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some break-throughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a user's behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs.

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Cited By

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  • (2024)Probabilistic graph model and neural network perspective of click models for web searchKnowledge and Information Systems10.1007/s10115-024-02145-z66:10(5829-5873)Online publication date: 6-Jun-2024
  • (2021)A Graph-Enhanced Click Model for Web SearchProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462895(1259-1268)Online publication date: 11-Jul-2021
  • (2016)Reducing Click and Skip Errors in Search Result RankingProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835838(183-192)Online publication date: 8-Feb-2016
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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    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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    Publication History

    Published: 19 July 2010

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    Author Tags

    1. bayesian model
    2. click log analysis
    3. post-click behavior

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2024)Probabilistic graph model and neural network perspective of click models for web searchKnowledge and Information Systems10.1007/s10115-024-02145-z66:10(5829-5873)Online publication date: 6-Jun-2024
    • (2021)A Graph-Enhanced Click Model for Web SearchProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462895(1259-1268)Online publication date: 11-Jul-2021
    • (2016)Reducing Click and Skip Errors in Search Result RankingProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835838(183-192)Online publication date: 8-Feb-2016
    • (2016)Decoding multi-click search behavior based on marginal utilityInformation Retrieval Journal10.1007/s10791-016-9289-z20:1(25-52)Online publication date: 22-Oct-2016
    • (2014)Characterizing multi-click search behavior and the risks and opportunities of changing results during useProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609588(515-524)Online publication date: 3-Jul-2014
    • (2013)A click model for time-sensitive queriesProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487859(147-148)Online publication date: 13-May-2013
    • (2013)Modeling click and relevance relationship for sponsored searchProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487844(119-120)Online publication date: 13-May-2013
    • (2013)Investigating the Promotional Effect of Green Signals in Sponsored Search Advertising Using Bayesian Parameter EstimationInformation Technology in Environmental Engineering10.1007/978-3-642-36011-4_3(25-37)Online publication date: 15-Sep-2013
    • (2012)Do ads compete or collaborate?Proceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398528(1839-1843)Online publication date: 29-Oct-2012
    • (2012)On caption bias in interleaving experimentsProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2396780(115-124)Online publication date: 29-Oct-2012
    • Show More Cited By

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