Assessing and predicting vertical intent for web queries

Ke Zhou, Ronan Cummins, Martin Halvey, Mounia Lalmas, Joemon M. Jose

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

    9 Citations (Scopus)


    Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.
    Original languageEnglish
    Title of host publicationAdvances in Information Retrieval
    Subtitle of host publication34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings
    Number of pages4
    ISBN (Electronic)9783642289972
    ISBN (Print)9783642289965
    Publication statusPublished - 2012

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Berlin Heidelberg
    ISSN (Print)0302-9743


    • web searching
    • diverse vertical content
    • inter-assessor agreement
    • search engine log


    Dive into the research topics of 'Assessing and predicting vertical intent for web queries'. Together they form a unique fingerprint.

    Cite this