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Telling How to Narrow it Down: Browsing Path Recommendation for Exploratory Search

Authors:

Maarten MarxAuthors Info & Claims

CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

Pages 369 - 372

https://doi.org/10.1145/3020165.3022155

Published: 07 March 2017 Publication History

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Abstract

Supporting exploratory search tasks with the help of structured data is an effective way to go beyond keyword search, as it provides an overview of the data, enables users to zoom in on their intent, and provides assistance during their navigation trails. However, finding a good starting point for a search episode in the given structure can still pose a considerable challenge, as users tend to be unfamiliar with exact, complex hierarchical structure. Thus, providing lookahead clues can be of great help and allow users to make better decisions on their search trajectory.

In this paper, we investigate the behaviour of users when a recommendation engine is employed along with the browsing tool in an exploratory search system. We make use of an exploratory search system that facilitates browsing by mapping the data on a hierarchical structure. We designed and developed a path recommendation engine as a feature for this system, which given a text query, ranks different browsing paths in the hierarchy based on their likelihood of covering relevant documents. We conduct a user study comparing the baseline system with the featured system.

Our main findings are as follows: We observe that, using the baseline system the users tend to explore the data in a breadth-first-like approach by visiting different data points at the same level of abstraction to choose one of them to expand and go deeper. Conversely, with browsing path recommendation (BPR) as a feature, the users tend to drive their search in a more depth-first-like approach by quickly going deep into the data hierarchy. While the users still incline to explore different parts of the search space by using BPR, they are able to restrain or augment their search focus more quickly and access smaller but more promising regions of the data. Therefore, they can complete their tasks with less time and effort

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Cited By

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Li YCrescenzi AWard ACapra R(2023)Thinking inside the boxJournal of the Association for Information Science and Technology10.1002/asi.2480174:9(1049-1066)Online publication date: 25-May-2023
https://dl.acm.org/doi/10.1002/asi.24801
Huurdeman HKamps J(2020)Designing Multistage Search Systems to Support the Information Seeking ProcessUnderstanding and Improving Information Search10.1007/978-3-030-38825-6_7(113-137)Online publication date: 30-May-2020
https://doi.org/10.1007/978-3-030-38825-6_7
Dehghani MJagfeld GAzarbonyad HOlieman AKamps JMarx MKamps JKanoulas Ede Rijke MFang HYilmaz E(2017)On Search Powered NavigationProceedings of the ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3121050.3121105(317-320)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1145/3121050.3121105

Index Terms

Telling How to Narrow it Down: Browsing Path Recommendation for Exploratory Search
1. Human-centered computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Search interfaces

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Published In

CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

March 2017

454 pages

ISBN:9781450346771

DOI:10.1145/3020165

Conference Chairs:
Ragnar Nordlie
Oslo & Akershus University College of Applied Sciences, Norway
,
Nils Pharo
Oslo and Akershus University College of Applied Sciences, Norway
,
Program Chairs:
Luanne Freund
University of British Columbia, Canada
,
Birger Larsen
Aalborg University, Denmark
,
Dan Russel
Google, USA

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2017

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Funding Sources

Netherlands Organization for Scientific Research
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CHIIR '17

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SIGIR

CHIIR '17: Conference on Human Information Interaction and Retrieval

March 7 - 11, 2017

Oslo, Norway

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CHIIR '17 Paper Acceptance Rate 10 of 48 submissions, 21%;

Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

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Li YCrescenzi AWard ACapra R(2023)Thinking inside the boxJournal of the Association for Information Science and Technology10.1002/asi.2480174:9(1049-1066)Online publication date: 25-May-2023
https://dl.acm.org/doi/10.1002/asi.24801
Huurdeman HKamps J(2020)Designing Multistage Search Systems to Support the Information Seeking ProcessUnderstanding and Improving Information Search10.1007/978-3-030-38825-6_7(113-137)Online publication date: 30-May-2020
https://doi.org/10.1007/978-3-030-38825-6_7
Dehghani MJagfeld GAzarbonyad HOlieman AKamps JMarx MKamps JKanoulas Ede Rijke MFang HYilmaz E(2017)On Search Powered NavigationProceedings of the ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3121050.3121105(317-320)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1145/3121050.3121105

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