Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems
This work addresses the vulnerability of e-commerce recommender systems to attacks, offering a domain-specific solution for imbalanced classification in attack detection.
The paper tackles the problem of detecting attacks in collaborative filtering recommender systems by proposing a re-scale AdaBoost method, which achieved improved detection performance compared to classical techniques like SVM, kNN, and AdaBoost on the MovieLens-100K dataset.
Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too "dull" to handle such imbalanced classification. In this paper, we improve detection performance from following two aspects. First, we extract well-designed features from user profiles based on the statistical properties of the diverse attack models, making hard classification task becomes easier to perform. Then, refer to the general idea of re-scale Boosting (RBoosting) and AdaBoost, we apply a variant of AdaBoost, called the re-scale AdaBoost (RAdaBoost) as our detection method based on extracted features. RAdaBoost is comparable to the optimal Boosting-type algorithm and can effectively improve the performance in some hard scenarios. Finally, a series of experiments on the MovieLens-100K data set are conducted to demonstrate the outperformance of RAdaBoost comparing with some classical techniques such as SVM, kNN and AdaBoost.