Top-k socio-spatial co-engaged location selection for social users
Article
Article Title | Top-k socio-spatial co-engaged location selection for social users |
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ERA Journal ID | 17876 |
Article Category | Article |
Authors | Hasan Haldar, Nur Al, Li, Jianxin, Ali, Mohammed Eunus, Cai, Taotao, Chen, Yunliang, Sellis, Timos and Reynolds, Mark |
Journal Title | IEEE Transactions on Knowledge and Data Engineering |
Journal Citation | 35 (5), pp. 5325-5340 |
Number of Pages | 16 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1041-4347 |
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. |
Keywords | LBSN; location selection in social networks; social graph computing; spatial database |
Related Output | |
Is supplemented by | https://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/LP180100750 |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460503. Data models, storage and indexing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Western Australia |
Deakin University | |
Bangladesh University of Engineering and Technology (BUET), Bangladesh | |
Macquarie University | |
China University of Geosciences, China | |
Swinburne University of Technology |
https://research.usq.edu.au/item/z6033/top-k-socio-spatial-co-engaged-location-selection-for-social-users
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