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Open AccessArticle

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

1
Software College, Northeastern University, Shenyang 110004, China
2
School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(6), 459; https://doi.org/10.3390/e20060459
Received: 17 May 2018 / Revised: 1 June 2018 / Accepted: 3 June 2018 / Published: 13 June 2018
(This article belongs to the Special Issue Research Frontier in Chaos Theory and Complex Networks)
Since complex search tasks are usually divided into subtasks, providing subtask-oriented query recommendations is an effective way to support complex search tasks. Currently, most subtask-oriented query recommendation methods extract subtasks from plain form search logs consisting of only queries and clicks, providing limited clues to identify subtasks. Meanwhile, for several decades, the Computer Human Interface (CHI)/Human Computer Interaction (HCI) communities have been working on new complex search tools for the purpose of supporting rich user interactions beyond just queries and clicks, and thus providing rich form search logs with more clues for subtask identification. In this paper, we researched the provision of subtask-oriented query recommendations by extracting thematic experiences from the rich form search logs of complex search tasks logged in a proposed visual data structure. We introduce the tree structure of the visual data structure and propose a visual-based subtask identification method based on the visual data structure. We then introduce a personalized PageRank-based method to recommend queries by ranking nodes on the network from the identified subtasks. We evaluated the proposed methods in experiments consisting of informative and tentative search tasks. View Full-Text
Keywords: complex search; subtask identification; query recommendation; personalized PageRank complex search; subtask identification; query recommendation; personalized PageRank
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MDPI and ACS Style

Zhao, Y.; Zhang, Y.; Zhang, B.; Gao, K.; Li, P. Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks. Entropy 2018, 20, 459.

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