Using the beta distribution technique to detect attacked items from collaborative filtering

Ping Yu Hsu, Jui Yi Chung, Yu Chin Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user's trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

Original languageEnglish
Pages (from-to)121-137
Number of pages17
JournalIntelligent Data Analysis
Volume25
Issue number1
DOIs
StatePublished - 2021

Keywords

  • Beta distribution push attack
  • Nuke attack
  • Recommendation systems
  • Shilling attacks

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