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  • 标题:NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data
  • 本地全文:下载
  • 作者:Ghassan F. Bati ; Vivek K. Singh
  • 期刊名称:Computers
  • 电子版ISSN:2073-431X
  • 出版年度:2021
  • 卷号:10
  • 期号:1
  • 页码:3
  • DOI:10.3390/computers10010003
  • 出版社:MDPI Publishing
  • 摘要:Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.
  • 关键词:deep learning; neighbor-aware deep learning; phone data; trust inference deep learning ; neighbor-aware deep learning ; phone data ; trust inference
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