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KDD 2020 | Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands

Accepted Papers

Finding Effective Geo-social Group for Impromptu Activities with Diverse Demands

Lu Chen: Swinburne University of Technology; Chengfei Liu: Swinburne University of Technology; Rui Zhou: Swinburne University of Technology; Jiajie Xu: Soochow University; Jeffrey Xu Yu: The Chinese University of Hong Kong; Jianxin Li: Deakin University


Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere for the activity and the spatial closeness of the geo-social group reduces the preparation time for the activity. Most existing works treat geo-social group search as a problem that finds a group satisfying a single social constraint while optimizing the spatial proximity. However, since different impromptu activities have diverse demands on attendees, e.g. an activity could require (or prefer) the attendees to have skills (or favorites) related to the activity, the existing works cannot find this kind of geo-social groups effectively. In this paper, we propose a novel geo-social group model, equipped with elegant keyword constraints, to fill this gap. We propose a novel search framework which first significantly narrows down the search space with theoretical guarantees and then efficiently finds the optimum result. To evaluate the effectiveness, we conduct experiments on real datasets, demonstrating the superiority of our proposed model. We conduct extensive experiments on large semi-synthetic datasets for justifying the efficiency of the proposed search algorithms.

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