Social-aware top-k spatial keyword search

Dingming Wu*, Yafei Li, Koon Kau CHOI, Jianliang XU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

25 Citations (Scopus)


The 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.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 15th International Conference on Mobile Data Management, IEEE MDM 2014
Number of pages10
ISBN (Electronic)9781479957057
Publication statusPublished - 5 Oct 2014
Event15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014 - Brisbane, Australia
Duration: 15 Jul 201418 Jul 2014

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
ISSN (Print)1551-6245


Conference15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014

Scopus Subject Areas

  • Engineering(all)


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