Top-k socio-spatial co-engaged location selection for social users

Article


Hasan Haldar, Nur Al, Li, Jianxin, Ali, Mohammed Eunus, Cai, Taotao, Chen, Yunliang, Sellis, Timos and Reynolds, Mark. 2023. "Top-k socio-spatial co-engaged location selection for social users." IEEE Transactions on Knowledge and Data Engineering. 35 (5), pp. 5325-5340. https://doi.org/10.1109/TKDE.2022.3151095
Article Title

Top-k socio-spatial co-engaged location selection for social users

ERA Journal ID17876
Article CategoryArticle
AuthorsHasan Haldar, Nur Al, Li, Jianxin, Ali, Mohammed Eunus, Cai, Taotao, Chen, Yunliang, Sellis, Timos and Reynolds, Mark
Journal TitleIEEE Transactions on Knowledge and Data Engineering
Journal Citation35 (5), pp. 5325-5340
Number of Pages16
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1041-4347
1558-2191
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2022.3151095
Web Address (URL)https://ieeexplore.ieee.org/document/9713727
Abstract

With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences for various socio-spatial activities and can be used to improve the quality of services in some applications such as recommendation systems, advertising, and group formation. To support such applications, in this paper, we formulate a new problem of identifying top-k Socio-Spatial co-engaged Location Selection (SSLS) for users in a social graph, that selects the best set of k locations from a large number of location candidates relating to the user and her friends. The selected locations should be (i) spatially and socially relevant to the user and her friends, and (ii) diversified both spatially and socially to maximize the coverage of friends in the socio-spatial space. This problem has been proved as NP-hard. To address such a challenging problem, we first develop an Exact solution by designing some pruning strategies based on derived bounds on diversity. To make the solution scalable for large datasets, we also develop an approximate solution by deriving relaxed bounds and advanced termination rules to filter out insignificant intermediate results. To further accelerate the efficiency, we present one fast exact approach and a meta-heuristic approximate approach by avoiding the repeated computation of diversity at the running time. Finally, we have performed extensive experiments to evaluate the performance of our proposed algorithms against three adapted existing methods using four large real-world datasets.

KeywordsLBSN; location selection in social networks; social graph computing; spatial database
Related Output
Is supplemented byhttps://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460503. Data models, storage and indexing
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsUniversity of Western Australia
Deakin University
Bangladesh University of Engineering and Technology (BUET), Bangladesh
Macquarie University
China University of Geosciences, China
Swinburne University of Technology
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