Open AccessUser abnormal behavior recommendation via multilayer network
Author(s) -
Chengyun Song,
Weiyi Liu,
Zhining Liu,
Xiaoyang Liu
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0224684
Subject(s) - computer science , recommender system , dilemma , popularity , usable , plaintext , precision and recall , encryption , data science , information retrieval , machine learning , artificial intelligence , world wide web , data mining , computer security , psychology , social psychology , philosophy , epistemology
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.