A Deep Learning Approach for Identifying User Interest from Targeted Advertising

Wonkyung Kim, Kukheon Lee, Sangjin Lee, Doowon Jeong

Research output: Contribution to journalArticlepeer-review

Abstract

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user’s devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user’s interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

Original languageEnglish
Pages (from-to)245-257
Number of pages13
JournalJournal of Information Processing Systems
Volume18
Issue number2
DOIs
Publication statusPublished - 2022 Apr

Keywords

  • Convolutional neural network (cnn)
  • Deep learning
  • Digital forensics
  • User interest
  • User profiling

ASJC Scopus subject areas

  • Software
  • Information Systems

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