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Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance

Published: 13 May 2013 Publication History

Abstract

In this paper, we present a log-based study on user search behavior comparisons on three different platforms: desktop, mobile and tablet. We use three-month search logs in 2012 from a commercial search engine for our study. Our objective is to better understand how and to what extent mobile and tablet searchers behave differently than desktop users. Our study spans a variety of aspects including query categorization, query length, search time distribution, search location distribution, user click patterns and so on. From our data set, we reveal that there are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them. Consequently, we propose a framework that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms. Experimental results demonstrate that by transferring knowledge from desktop search, search relevance on mobile and tablet can be greatly improved.

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      Published In

      cover image ACM Other conferences
      WWW '13: Proceedings of the 22nd international conference on World Wide Web
      May 2013
      1628 pages
      ISBN:9781450320351
      DOI:10.1145/2488388

      Sponsors

      • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
      • CGIBR: Comite Gestor da Internet no Brazil

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 May 2013

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      Author Tags

      1. mobile search
      2. search result ranking
      3. tablet search
      4. user behavior analysis

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      • Research-article

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      WWW '13
      Sponsor:
      • NICBR
      • CGIBR
      WWW '13: 22nd International World Wide Web Conference
      May 13 - 17, 2013
      Rio de Janeiro, Brazil

      Acceptance Rates

      WWW '13 Paper Acceptance Rate 125 of 831 submissions, 15%;
      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      Cited By

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      • (2024)Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions dataAslib Journal of Information Management10.1108/AJIM-04-2023-0107Online publication date: 10-Jan-2024
      • (2023)Searching Online for Art and Culture: User Behavior AnalysisFuture Internet10.3390/fi1506021115:6(211)Online publication date: 11-Jun-2023
      • (2023)An F-shape Click Model for Information Retrieval on Multi-block Mobile PagesProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570365(1057-1065)Online publication date: 27-Feb-2023
      • (2023)Shop by image: characterizing visual search in e-commerceInformation Retrieval Journal10.1007/s10791-023-09418-126:1Online publication date: 3-Mar-2023
      • (2022)Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery ChamberSensors10.3390/s2219762822:19(7628)Online publication date: 8-Oct-2022
      • (2022)User Evaluation and Metrics Analysis of a Prototype Web-Based Federated Search Engine for Art and Cultural HeritageInformation10.3390/info1306028513:6(285)Online publication date: 4-Jun-2022
      • (2021)Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile AssistantsACM Transactions on Information Systems10.1145/344767839:3(1-30)Online publication date: 5-May-2021
      • (2021)Mobile vs desktop user search behaviours of the 1300K site, a Korean shopping search engineThe Electronic Library10.1108/EL-09-2020-026139:2(239-257)Online publication date: 15-Jun-2021
      • (2021)On Latency of E-Commerce PlatformsJournal of Organizational Computing and Electronic Commerce10.1080/10919392.2021.1882240(1-17)Online publication date: 24-Feb-2021
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