demonstration

FindiLike: preference driven entity search

Authors:

Kavita Ganesan,

ChengXiang ZhaiAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 345 - 348

https://doi.org/10.1145/2187980.2188045

Published: 16 April 2012 Publication History

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Abstract

Traditional web search engines enable users to find documents based on topics. However, in finding entities such as restaurants, hotels and products, traditional search engines fail to suffice as users are often interested in finding entities based on structured attributes such as price and brand and unstructured information such as opinions of other web users. In this paper, we showcase a preference driven search system, that enables users to find entities of interest based on a set of structured preferences as well as unstructured opinion preferences. We demonstrate our system in the context of hotel search.

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Cited By

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Bashir S(2017)Ranking entities on the basis of users' opinionsMultimedia Tools and Applications10.1007/s11042-015-3022-676:1(59-81)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11042-015-3022-6
Park DLiu MZhai CWang HBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Leveraging User Reviews to Improve Accuracy for Mobile App RetrievalProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767759(533-542)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767759
Ganesan KZhai CBalog KElsweiler DKanoulas EKelly LSmucker M(2013)FindiLikeProceedings of the 2013 workshop on Living labs for information retrieval evaluation10.1145/2513150.2513163(19-22)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1145/2513150.2513163

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Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 16 April 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Bashir S(2017)Ranking entities on the basis of users' opinionsMultimedia Tools and Applications10.1007/s11042-015-3022-676:1(59-81)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11042-015-3022-6
Park DLiu MZhai CWang HBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Leveraging User Reviews to Improve Accuracy for Mobile App RetrievalProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767759(533-542)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767759
Ganesan KZhai CBalog KElsweiler DKanoulas EKelly LSmucker M(2013)FindiLikeProceedings of the 2013 workshop on Living labs for information retrieval evaluation10.1145/2513150.2513163(19-22)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1145/2513150.2513163

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FindiLike: a preference driven entity search engine for evaluating entity retrieval and opinion summarization

Multi-probe LSH: efficient indexing for high-dimensional similarity search

An improved algorithm finding nearest neighbor using Kd-trees

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