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Conference Paper: Social-Aware Top-k Spatial Keyword Search

TitleSocial-Aware Top-k Spatial Keyword Search
Authors
Issue Date2014
PublisherI E E E.
Citation
The 15th IEEE International Conference on Mobile Data Management (MDM), Brisbane, Australia, 15-18 July 2014. In I E E E International Conference on Mobile Data Management Proceedings, 2014, v. 1, p. 235-244 How to Cite?
AbstractThe boom of the spatial web has enabled spatial keyword queries that take a user location and multiple search keywords as arguments and return the objects that are spatially and textually relevant to these arguments. Recently, utilizing social data to improve search results, normally by giving a higher rank to the content generated or consumed by the searcher's friends in the social network, has been studied in the information retrieval (IR) community. However, little attention has been drawn to the integration of social factors into spatial keyword query processing. In this paper, we propose a novel spatial keyword query, Social-aware top-k Spatial Keyword (SkSK) query, which enriches the semantics of the conventional spatial keyword query by introducing a new social relevance attribute. A hybrid index structure, called Social Network-aware IR-tree (SNIR-tree), is proposed for the processing of SkSK queries. To further improve the query response time, an x-hop localized algorithm is developed. Empirical results demonstrate that the proposed index and algorithms are capable of excellent performance.
Persistent Identifierhttp://hdl.handle.net/10722/198602
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWu, Den_US
dc.contributor.authorLi, Yen_US
dc.contributor.authorChoi, Ben_US
dc.contributor.authorXu, Jen_US
dc.date.accessioned2014-07-07T08:09:40Z-
dc.date.available2014-07-07T08:09:40Z-
dc.date.issued2014en_US
dc.identifier.citationThe 15th IEEE International Conference on Mobile Data Management (MDM), Brisbane, Australia, 15-18 July 2014. In I E E E International Conference on Mobile Data Management Proceedings, 2014, v. 1, p. 235-244en_US
dc.identifier.isbn9781479957057-
dc.identifier.issn1551-6245-
dc.identifier.urihttp://hdl.handle.net/10722/198602-
dc.description.abstractThe boom of the spatial web has enabled spatial keyword queries that take a user location and multiple search keywords as arguments and return the objects that are spatially and textually relevant to these arguments. Recently, utilizing social data to improve search results, normally by giving a higher rank to the content generated or consumed by the searcher's friends in the social network, has been studied in the information retrieval (IR) community. However, little attention has been drawn to the integration of social factors into spatial keyword query processing. In this paper, we propose a novel spatial keyword query, Social-aware top-k Spatial Keyword (SkSK) query, which enriches the semantics of the conventional spatial keyword query by introducing a new social relevance attribute. A hybrid index structure, called Social Network-aware IR-tree (SNIR-tree), is proposed for the processing of SkSK queries. To further improve the query response time, an x-hop localized algorithm is developed. Empirical results demonstrate that the proposed index and algorithms are capable of excellent performance.-
dc.languageengen_US
dc.publisherI E E E.-
dc.relation.ispartofI E E E International Conference on Mobile Data Management Proceedingsen_US
dc.rightsI E E E International Conference on Mobile Data Management Proceedings. Copyright © I E E E.-
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSocial-Aware Top-k Spatial Keyword Searchen_US
dc.typeConference_Paperen_US
dc.identifier.emailWu, D: dmwu@cs.hku.hken_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/MDM.2014.35-
dc.identifier.hkuros230036en_US
dc.identifier.volume1-
dc.identifier.spage235-
dc.identifier.epage244-
dc.publisher.placeUnited States-

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