Top-k Socio-Spatial Co-Engaged Location Selection for Social Users

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 rec...

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Published in:	IEEE transactions on knowledge and data engineering Vol. 35; no. 5; pp. 5325 - 5340
Main Authors:	Haldar, Nur Al Hasan, Li, Jianxin, Ali, Mohammed Eunus, Cai, Taotao, Chen, Yunliang, Sellis, Timos, Reynolds, Mark
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Advertising Algorithms Australia Cultural differences Datasets Elections Exact solutions Heuristic methods LBSN Location based services location selection in social networks Organizations Recommender systems Social factors social graph computing Social networking (online) Social networks spatial database Spatial databases
ISSN:	1041-4347, 1558-2191
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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 S ocio- S patial co-engaged L ocation S election ( SSLS ) for users in a social graph, that selects the best set of <inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3151095.gif"/> </inline-formula> 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.
AbstractList	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 S ocio- S patial co-engaged L ocation S election ( SSLS ) for users in a social graph, that selects the best set of [Formula Omitted] 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. 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 S ocio- S patial co-engaged L ocation S election ( SSLS ) for users in a social graph, that selects the best set of <inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3151095.gif"/> </inline-formula> 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.
Author	Li, Jianxin Cai, Taotao Sellis, Timos Haldar, Nur Al Hasan Chen, Yunliang Ali, Mohammed Eunus Reynolds, Mark
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SubjectTerms	Advertising Algorithms Australia Cultural differences Datasets Elections Exact solutions Heuristic methods LBSN Location based services location selection in social networks Organizations Recommender systems Social factors social graph computing Social networking (online) Social networks spatial database Spatial databases
Title	Top-k Socio-Spatial Co-Engaged Location Selection for Social Users
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