Abstract
In this paper, we propose Geo-Social Keyword (GSK) search, which enables the retrieval of users, points of interest (POIs), or keywords that satisfy geographic, social, and/or textual criteria. We first introduce a general GSK framework that covers a wide range of real-world tasks, including advertisement, context-based search, and market analysis. Then, we present three concrete GSK queries: (i) NPRU that returns the top-k users based on their spatial proximity to a given query location, their popularity, and their similarity to an input set of terms; (ii) NSTP that outputs the top-k POIs based on their proximity to a user v, the number of check-ins by friends of v, and their similarity to a set of terms; (iii) FSKR that discovers the top-k keywords based on their frequency in pairs of friends located within a spatial area. For each query, we develop a processing algorithm that utilizes a novel hybrid index. Finally, we evaluate our framework with thorough experiments using real datasets.
R. Ahuja—Supported by GRF grant 617412 from Hong Kong RGC.
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Notes
- 1.
F should satisfy the condition \(\forall o, o': f_g(o)\ge f_g(o') \wedge f_s(o)\ge f_s(o') \wedge f_t(o)\ge f_t(o') \Rightarrow F(o)\ge F(o')\).
- 2.
A bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set [6].
- 3.
Additional constraints in this case could restrict the top-k POIs to be in a certain area, or enforce certain properties (e.g., restaurant must be open after 10 pm).
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Ahuja, R., Armenatzoglou, N., Papadias, D., Fakas, G.J. (2015). Geo-Social Keyword Search. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_23
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