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Finding effective geo-social group for impromptu activities with diverse demands
conference contribution
posted on 2020-01-01, 00:00 authored by Lu Chen, Chengfei Liu, Rui Zhou, Jiajie Xu, Jeffrey Xu Yu, Jianxin LiJianxin LiGeo-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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Event
KDD '20 Knowledge Discovery and Data Mining. International Conference (2020 : 26th : Virtual Event, USA)Pagination
698 - 708Publisher
Association for Computing Machinery (ACM)Location
Virtual Event, USAPlace of publication
New York, N.Y.Publisher DOI
Start date
2020-08-23End date
2020-08-27ISBN-13
9781450379984Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2020, The AuthorsTitle of proceedings
KDD '20 : Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningUsage metrics
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