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

Bo Heng Chen, Cheng Te Li, Kun Ta Chuang, Jun Pang, Yang Zhang

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)539-569
期刊Knowledge and Information Systems
出版狀態Published - 2019 6月 4

All Science Journal Classification (ASJC) codes

  • 軟體
  • 資訊系統
  • 人機介面
  • 硬體和架構
  • 人工智慧


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