DocumentCode :
1763833
Title :
Joint Search by Social and Spatial Proximity
Author :
Mouratidis, Kyriakos ; Jing Li ; Yu Tang ; Mamoulis, Nikos
Author_Institution :
Sch. of Inf. Syst., Singapore Manage. Univ., Singapore, Singapore
Volume :
27
Issue :
3
fYear :
2015
fDate :
March 1 2015
Firstpage :
781
Lastpage :
793
Abstract :
The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and enhance them with elaborate optimizations. Finally, we use real social network data to empirically verify the efficiency and efficacy of our solutions.
Keywords :
query processing; recommender systems; social networking (online); social sciences computing; friend recommendation; joint search; location-based features; query type; social networks diffusion; social proximity; spatial proximity; top-k search; two-fold semantics; Data structures; Distributed databases; Educational institutions; Euclidean distance; Indexes; Social network services; Tin; Location-based social networks; friend recommendation; top-k search in multiple domains;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2014.2339838
Filename :
6858087
Link To Document :
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