Using Query-Log Based Collective Intelligence to Generate Query Suggestions for Tagged Content Search

Conference paper

DOI: 10.1007/978-3-319-19890-3_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9114)
Cite this paper as:
Guijt D., Hauff C. (2015) Using Query-Log Based Collective Intelligence to Generate Query Suggestions for Tagged Content Search. In: Cimiano P., Frasincar F., Houben GJ., Schwabe D. (eds) Engineering the Web in the Big Data Era. ICWE 2015. Lecture Notes in Computer Science, vol 9114. Springer, Cham

Abstract

One of the standard features of today’s major Web search engines are query suggestions, which aid the user in the formulation of their search queries. Over the years, a number of different approaches have been proposed which have commonly been evaluated in the standard Web search setting. In this work, we build a query suggestion pipeline based on the collective intelligence stored in log data collected from a more constrained search engine which uses tags to index the content. This constrained environment, though large-scale, differs considerably from standard Web search with respect to its users, indexing process and Web coverage. We implement a number of suggestion approaches based on query-flow and term-query graph models and investigate to what extent they are applicable in this more constrained environment.

Keywords

Query suggestions Query-flow graphs Search sessions Collective intelligence Tags Tagged content 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.SanomaHoofddorpThe Netherlands
  2. 2.Web Information SystemsTU DelftDelftThe Netherlands

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