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Collaborative filtering recommendation using fusing criteria against shilling attacks
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OA
AI
DOI：10.1080/09540091.2022.2078280.png)
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Abstract
En 中文
The collaborative filtering recommendation technique (CFR) is one of the techniques used in recommended systems, in which the most proximal neighbours to a target user are selected. Their profiles are used to predict rating for items as yet unrated by that target user. However, malicious users inject fake user profiles to destroy the security and reliability of the recommender systems, which is called shilling attacks. Therefore, it is crucial to improve the recommendation technique against shilling attacks. Malicious users use a single method to perform shilling attacks. Intuitively, fusing multiple criteria to construct CFR can effectively resist shilling attacks. A novel CFR is proposed against shilling attacks (called CFR-F). In our approach, a similar interest users' resource set is obtained first by integrating users' dynamic interest model and social tags. Then, a similar interest user resource set is selected according to a strategy that selects preference influence weight based on user background. Our experimental results show that our approach can recommend accurate information resources and has a lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) than traditional techniques by 50% and 20%, respectively.
Keywords:
Shilling attack
user context
dynamic social behaviour
social tags