Using the beta distribution technique to detect attacked items from collaborative filtering
Autor: | Jui Yi Chung, Yu Chin Liu, Ping-Yu Hsu |
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Rok vydání: | 2021 |
Předmět: |
Computer science
02 engineering and technology Recommender system computer.software_genre Theoretical Computer Science Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Data mining computer Beta distribution |
Zdroj: | Intelligent Data Analysis. 25:121-137 |
ISSN: | 1571-4128 1088-467X |
Popis: | 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%. |
Databáze: | OpenAIRE |
Externí odkaz: | |
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