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An active learning-based approach for location-aware acquaintance inference

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Abstract

With the popularity of mobile devices and various sensors, the local geographical activities of human beings can be easily accessed than ever. Yet due to the privacy concern, it is difficult to acquire the social connections among people possessed by services providers, which can benefit applications such as identifying terrorists and recommender systems. In this paper, we propose the location-aware acquaintance inference (LAI) problem, which aims at finding the acquaintances for any given query individual based on solely people’s local geographical activities, such as geo-tagged posts in Instagram and meeting events in Meetup, within a targeted geo-spatial area. We propose to leverage the concept of active learning to tackle the LAI problem. We develop a novel semi-supervised model, active learning-enhanced random walk (ARW), which imposes the idea of active learning into the technique of random walk with restart (RWR) in an activity graph. Specifically, we devise a series of candidate selection strategies to select unlabeled individuals for labeling and perform the different graph refinement mechanisms that reflect the labeling feedback to guide the RWR random surfer. Experiments conducted on Instagram and Meetup datasets exhibit the promising performance, compared with a set of state-of-the-art methods. With a series of empirical settings, ARW is demonstrated to derive satisfying results of acquaintance inference in different real scenarios.

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Notes

  1. The connection is aborted on April 20, 2016 (https://www.instagram.com/developer/changelog/).

  2. http://www.faceplusplus.com/.

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Acknowledgements

This work was supported in part by Ministry of Science and Technology, ROC, under Contract 105-2221-E-006-140-MY2, 107-2636-E-006-002 and 106-3114-E-006-002. In addition, we also thank the support from Academia Sinica under the Grant AS-107-TP-A05. Also, we would like to thank anonymous reviewers and the editor for their very useful comments and suggestions.

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Authors and Affiliations

  1. Graduate Program of Multimedia Systems and Intelligent Computing, National Cheng Kung University and Academia Sinica, Tainan, Taiwan

    Bo-Heng Chen

  2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Bo-Heng Chen & Kun-Ta Chuang

  3. Department of Statistics, National Cheng Kung University, Tainan, Taiwan

    Cheng-Te Li

  4. Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Jun Pang

  5. CISPA, Saarland Informatics Campus, Saarbrücken, Germany

    Yang Zhang

  6. Institute of Data Science, National Cheng Kung University, Tainan, Taiwan

    Cheng-Te Li

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  1. Bo-Heng Chen
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  2. Cheng-Te Li
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  3. Kun-Ta Chuang
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  4. Jun Pang
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  5. Yang Zhang
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Correspondence to Kun-Ta Chuang.

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Chen, BH., Li, CT., Chuang, KT. et al. An active learning-based approach for location-aware acquaintance inference. Knowl Inf Syst 59, 539–569 (2019). https://doi.org/10.1007/s10115-018-1196-8

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