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CloseUp—A Community-Driven Live Online Search Engine

Published: 27 August 2019 Publication History

Abstract

Search engines are still the most common way of finding information on the Web. However, they are largely unable to provide satisfactory answers to time- and location-specific queries. Such queries can best and often only be answered by humans that are currently on-site. Although online platforms for community question answering are very popular, very few exceptions consider the notion of users’ current physical locations. In this article, we present CloseUp, our prototype for the seamless integration of community-driven live search into a Google-like search experience. Our efforts focus on overcoming the defining differences between traditional Web search and community question answering, namely the formulation of search requests (keyword-based queries vs. well-formed questions) and the expected response times (milliseconds vs. minutes/hours). To this end, the system features a deep learning pipeline to analyze submitted queries and translate relevant queries into questions. Searching users can submit suggested questions to a community of mobile users. CloseUp provides a stand-alone mobile application for submitting, browsing, and replying to questions. Replies from mobile users are presented as live results in the search interface. Using a field study, we evaluated the feasibility and practicability of our approach.

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  • (2021)Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning FrameworkACM Transactions on Internet Technology10.1145/338377921:3(1-21)Online publication date: 16-Jun-2021

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Published In

cover image ACM Transactions on Internet Technology
ACM Transactions on Internet Technology  Volume 19, Issue 3
Special Section on Advances in Internet-Based Collaborative Technologies
August 2019
289 pages
ISSN:1533-5399
EISSN:1557-6051
DOI:10.1145/3329912
  • Editor:
  • Ling Liu
Issue’s Table of Contents
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Publication History

Published: 27 August 2019
Accepted: 01 November 2018
Revised: 01 October 2018
Received: 01 January 2018
Published in TOIT Volume 19, Issue 3

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Author Tags

  1. Live online search
  2. collaborative service
  3. community question answering
  4. crowdsourcing
  5. query transformation
  6. social computing

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  • Research-article
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  • Refereed

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  • National Research Foundation, Prime Minister's Office, Singapore, under its Strategic Capability Research Centres Funding Initiative

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  • (2021)Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning FrameworkACM Transactions on Internet Technology10.1145/338377921:3(1-21)Online publication date: 16-Jun-2021

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