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