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

Nur Al Hasan Haldar, Jianxin Li, Mohammed Eunus Ali, Taotao Cai, Yunliang Chen, Timos Sellis, Mark Reynolds

Research output: Contribution to journalArticlepeer-review

8 Citations (Web of Science)


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. To address the NP-hard and challenging problem, we first develop an exact solution by designing some pruning strategies, and also develop an approximate solution by deriving relaxed bounds and advanced termination rules. To accelerate the efficiency, we further develop a fast exact approach and a meta-heuristic approximate approach. Finally, extensive experiments are conducted to evaluate the performance of our proposed algorithms against three adapted existing methods using four real-world datasets.

Original languageEnglish
Pages (from-to)5325-5340
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number5
Publication statusPublished - 1 May 2023


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