TB-Structure : Collective Intelligence for Exploratory Keyword Search
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). Lecture Notes in Computer Science, 10151. Springer International Publishing. doi:10.1007/978-3-319-53640-8_15
Published inLecture Notes in Computer Science;10151
© 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.
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.