User abnormal behavior recommendation via multilayer network.

Clicks: 221
ID: 82684
2019
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Abstract
With the growing popularity of online services such as online banking and online shopping, one of the essential research topics is how to build a privacy-preserving user abnormal behavior recommendation system. However, a machine-learning based system may present a dilemma. On one aspect, such system requires large volume of features to pre-train the model, but on another aspect, it is challenging to design usable features without looking to plaintext private data. In this paper, we propose an unorthodox approach involving graph analysis to resolve this dilemma and build a novel private-preserving recommendation system under a multilayer network framework. In experiments, we use a large, state-of-the-art dataset (containing more than 40,000 nodes and 43 million encrypted features) to evaluate the recommendation ability of our system on abnormal user behavior, yielding an overall precision rate of around 0.9, a recall rate of 1.0, and an F1-score of around 0.94. Also, we have also reported a linear time complexity for our system. Last, we deploy our system on the "Wenjuanxing" crowd-sourced system and "Amazon Mechanical Turk" for other users to evaluate in all aspects. The result shows that almost all feedbacks have achieved up to 85% satisfaction.
Reference Key
song2019userplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Song, Chengyun;Liu, Weiyi;Liu, Zhining;Liu, Xiaoyang;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0224684
URL
Keywords
lpc purchase intention n2 hedonic products online shopping festival utilitarian products

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