Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

The web is increasingly being accessed from mobile devices, and studies suggest that a large fraction of keyword-based search engine queries have local intent, meaning that users are interested in local content and that the underlying ranking function should take into account both relevance to the query keywords and the query location. A key challenge in being able to make progress on the design of ranking functions is to be able to assess the quality of the results returned by ranking functions.
We propose a model that synthesizes a ranking of points of interest from answers to crowdsourced pairwise relevance questions. To evaluate the model, we propose an innovative methodology that enables evaluation of the quality of synthesized rankings in a simulated setting. We report on an experimental evaluation based on the methodology that shows that the proposed model produces promising results in pertinent settings and that it is capable of outperforming an approach based on majority voting.
Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Geographic Information Retrieval
Number of pages10
PublisherAssociation for Computing Machinery
Publication date2015
Article number15
ISBN (Print)978-1-4503-3937-7
DOIs
Publication statusPublished - 2015
Event9th Workshop on Geographic Information Retrieval - Paris, France
Duration: 26 Nov 201527 Nov 2015
Conference number: 9
http://www.geo.uzh.ch/~rsp/gir15/

Conference

Conference9th Workshop on Geographic Information Retrieval
Number9
CountryFrance
CityParis
Period26/11/201527/11/2015
Internet address

Fingerprint

Search engines
Mobile devices

Cite this

Keles, I., Saltenis, S., & Jensen, C. S. (2015). Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing. In Proceedings of the 9th Workshop on Geographic Information Retrieval [15] Association for Computing Machinery. https://doi.org/10.1145/2837689.2837705
Keles, Ilkcan ; Saltenis, Simonas ; Jensen, Christian Søndergaard. / Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing. Proceedings of the 9th Workshop on Geographic Information Retrieval. Association for Computing Machinery, 2015.
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abstract = "The web is increasingly being accessed from mobile devices, and studies suggest that a large fraction of keyword-based search engine queries have local intent, meaning that users are interested in local content and that the underlying ranking function should take into account both relevance to the query keywords and the query location. A key challenge in being able to make progress on the design of ranking functions is to be able to assess the quality of the results returned by ranking functions.We propose a model that synthesizes a ranking of points of interest from answers to crowdsourced pairwise relevance questions. To evaluate the model, we propose an innovative methodology that enables evaluation of the quality of synthesized rankings in a simulated setting. We report on an experimental evaluation based on the methodology that shows that the proposed model produces promising results in pertinent settings and that it is capable of outperforming an approach based on majority voting.",
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Keles, I, Saltenis, S & Jensen, CS 2015, Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing. in Proceedings of the 9th Workshop on Geographic Information Retrieval., 15, Association for Computing Machinery, 9th Workshop on Geographic Information Retrieval, Paris, France, 26/11/2015. https://doi.org/10.1145/2837689.2837705

Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing. / Keles, Ilkcan; Saltenis, Simonas; Jensen, Christian Søndergaard.

Proceedings of the 9th Workshop on Geographic Information Retrieval. Association for Computing Machinery, 2015. 15.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AB - The web is increasingly being accessed from mobile devices, and studies suggest that a large fraction of keyword-based search engine queries have local intent, meaning that users are interested in local content and that the underlying ranking function should take into account both relevance to the query keywords and the query location. A key challenge in being able to make progress on the design of ranking functions is to be able to assess the quality of the results returned by ranking functions.We propose a model that synthesizes a ranking of points of interest from answers to crowdsourced pairwise relevance questions. To evaluate the model, we propose an innovative methodology that enables evaluation of the quality of synthesized rankings in a simulated setting. We report on an experimental evaluation based on the methodology that shows that the proposed model produces promising results in pertinent settings and that it is capable of outperforming an approach based on majority voting.

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Keles I, Saltenis S, Jensen CS. Synthesis of Partial Rankings of Points of Interest Using Crowdsourcing. In Proceedings of the 9th Workshop on Geographic Information Retrieval. Association for Computing Machinery. 2015. 15 https://doi.org/10.1145/2837689.2837705