Search Interrupted Understanding and Predicting Search Task Continuation

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JACIII Vol.24 No.3 pp. 316-325

doi: 10.20965/jaciii.2020.p0316

(2020)

Paper:

Views over last 60 days: 484

Estimation of Search Intents from Query to Context Search Engine

^*Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

^**College of Science and Technology, National Chi Nan University
No.1 University Road, Puli, Nantou 54561, Taiwan

Received:

September 28, 2019

Accepted:

February 18, 2020

Published:

May 20, 2020

Keywords:

search engine, time series data, search intent, learning to rank

Abstract

Screenshot of Context Search Engine (CSE) result page

This paper estimates users' search intents when using the context search engine (CSE) by analyzing submitted queries. Recently, due to the increase in the amount of information on the Web and the diversification of information needs, the gap between user's information needs and a basic search function provided by existing web search engines becomes larger. As a solution to this problem, the CSE that limits its tasks to answer questions about temporal trends has been proposed. It provides three primitive search functions, which users can use in accordance with their purposes. Furthermore, if the system can estimate users' search intents, it can provide more user-friendly services that contribute the improvement of search efficiency. Aiming at estimating users' search intents only from submitted queries, this paper analyzes the characteristics of queries in terms of typical search intents when using CSE, and defines classification rules. To show the potential use of the estimated search intents, this paper introduces a learning to rank into CSE. Experimental results show that MAP (mean average precision) is improved by learning rank models separately for different search intents.

Cite this article as:

Y. Takama, T. Tezuka, H. Shibata, and L. Chen, "Estimation of Search Intents from Query to Context Search Engine," J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.3, pp. 316-325, 2020.

Data files:

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Last updated on Oct. 14, 2022

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