TB-Structure : Collective Intelligence for Exploratory Keyword Search

Abstract
In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s intents. We used experiments to demonstrate both: the storage compactness and inference potential of the proposed structure. See presentation slides: https://ai.it.jyu.fi/IKC-2016.pptx
Main Authors
Format
Conferences Conference paper
Published
2017
Series
Subjects
Publication in research information system
Publisher
Springer International Publishing
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201711164273Use this for linking
Parent publication ISBN
978-3-319-53639-2
Review status
Peer reviewed
ISSN
0302-9743
DOI
https://doi.org/10.1007/978-3-319-53640-8_15
Conference
International Keystone conference
Language
English
Published in
Lecture Notes in Computer Science
Is part of publication
Semantic Keyword-Based Search on Structured Data Sources. COST Action IC1302 Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8–9, 2016, Revised Selected Papers
Citation
  • Terziyan, V., Golovianko, M., & Cochez, M. (2017). TB-Structure : Collective Intelligence for Exploratory Keyword Search. In A. Calì, D. Gorgan, & M. Ugarte (Eds.), Semantic Keyword-Based Search on Structured Data Sources. COST Action IC1302 Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8–9, 2016, Revised Selected Papers (pp. 171-178). Springer International Publishing. Lecture Notes in Computer Science, 10151. https://doi.org/10.1007/978-3-319-53640-8_15
License
Open Access
Copyright© Springer International Publishing AG 2017. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.

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