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Entropy, Vol. 20, Pages 459: Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Entropy, Vol. 20, Pages 459: Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Entropy doi: 10.3390/e20060459

Authors: Yuli Zhao Yin Zhang Bin Zhang Kening Gao Pengfei Li

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.

Authors:   Zhao, Yuli ; Zhang, Yin ; Zhang, Bin ; Gao, Kening ; Li, Pengfei
Journal:   Entropy
Volume:   20
edition:   6
Year:   2018
Pages:   459
DOI:   10.3390/e20060459
Publication date:   13-Jun-2018
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