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A new visual search interface for web browsing

Published: 09 February 2009 Publication History

Abstract

We introduce a new visual search interface for search engines. The interface is a user-friendly and informative graphical front-end for organizing and presenting search results in the form of topic groups. Such a semantics-oriented search result presentation is in contrast with conventional search interfaces which present search results according to the physical structures of the information. Given a user query, our interface first retrieves relevant online materials via a third-party search engine. And then we analyze the semantics of search results to detect latent topics in the result set. Once the topics are detected, we map the search result pages into topic clusters. According to the topic clustering result, we divide the available screen space for our visual interface into multiple topic displaying regions, one for each topic. For each topic's displaying region, we summarize the information contained in the search results under the corresponding topic so that only key messages will be displayed. With this new visual search interface, users are conveyed the key information in the search results expediently. With the key information, users can navigate to the final, desired results with less effort and time than conventional searching. Supplementary materials for this paper are available at http://www.cs.hku.hk/~songhua/visualsearch/.

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Cited By

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  • (2017)Don't Just Swipe Left, Tell Me WhyProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025212(469-480)Online publication date: 7-Mar-2017
  • (2015)Using Textual Semantic Similarity to Improve Clustering Quality of Web Video Search Results2015 Seventh International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE.2015.47(156-161)Online publication date: Oct-2015
  • (2014)Browse to search, visualize to exploreComputers in Human Behavior10.1016/j.chb.2014.07.01039:C(145-153)Online publication date: 1-Sep-2014
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Scott Arthur Moody

As the informational Web continues to increase at exponential rates, tools that manage and simplify this complexity are in high demand. How else would one find out "the effect of chocolate on a person's emotion__?__"; this question, in fact, is used in the study. This paper is an excellent addition to the field, describing a new visual search interface that can answer those questions in new and arguably simpler ways. The paper also lays out a foundation for how new tools should be compared to the norm, mainly Google. The authors incorporated a study group's flyoff between the two search capabilities, presenting statistics of how their visual digest capability could usually answer a set of 30 obscure queries, like the chocolate one, faster than traditional approaches. Their approach basically extends the normal textual search retrieval results and, through semantic-based document clustering, presents aggregated results, including select document images; their "visual interface is content or semantics oriented rather than physical link or document oriented." By using various topic clustering approaches and combining them with the visual displays, higher and potentially more accurate results decrease the time needed to answer questions. The second half of the paper details the user study, the 30 questions, and various statistics comparing their favorable results to the Google benchmark. Overall, this paper will help readers understand the limitations of current Web search approaches. The authors then show how document digest approaches like theirs, if done efficiently and effectively, will form the base of the next-generation visual search interfaces for Web browsing. Online Computing Reviews Service

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cover image ACM Conferences
WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining
February 2009
314 pages
ISBN:9781605583907
DOI:10.1145/1498759
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 February 2009

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Author Tags

  1. document summarization
  2. online browsing and navigation
  3. visual search interface
  4. web search

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Cited By

View all
  • (2017)Don't Just Swipe Left, Tell Me WhyProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025212(469-480)Online publication date: 7-Mar-2017
  • (2015)Using Textual Semantic Similarity to Improve Clustering Quality of Web Video Search Results2015 Seventh International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE.2015.47(156-161)Online publication date: Oct-2015
  • (2014)Browse to search, visualize to exploreComputers in Human Behavior10.1016/j.chb.2014.07.01039:C(145-153)Online publication date: 1-Sep-2014
  • (2013)SerefindProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487925(289-292)Online publication date: 13-May-2013
  • (2012)SWiPEProceedings of the 21st International Conference on World Wide Web10.1145/2187980.2188036(309-312)Online publication date: 16-Apr-2012
  • (2012)A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniquesInformation Processing and Management: an International Journal10.1016/j.ipm.2011.12.00448:4(698-724)Online publication date: 1-Jul-2012
  • (2012)Semantifying queries over large-scale Web search enginesJournal of Internet Services and Applications10.1007/s13174-012-0068-93:3(255-268)Online publication date: 12-Sep-2012
  • (2011)Personalisation in the wildProceedings of the 22nd ACM conference on Hypertext and hypermedia10.1145/1995966.1995979(73-82)Online publication date: 6-Jun-2011
  • (2011)Predicting Trust and Distrust in Social Networks2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing10.1109/PASSAT/SocialCom.2011.56(418-424)Online publication date: Oct-2011
  • (2011)Clustering Web video search results based on integration of multiple featuresWorld Wide Web10.1007/s11280-010-0097-x14:1(53-73)Online publication date: 1-Jan-2011
  • Show More Cited By

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