Abstract
The recommender system based on collaborative filtering is vulnerable to shilling attacks due to its open nature. With the wide employment of recommender systems, an increasing number of attackers are disordering the system in order to benefit from the manipulated recommendation results. Therefore, how to effectively detect shilling attacks now becomes more and more crucial. Most existing detection models recognize attackers in statistics-based manners. However, they failed in capturing the fine-grained interactions between users and items, leading to a degradation in detection accuracy. In this paper, inspired by the success of word embedding models, we propose a collaborative shilling detection model, CoDetector, which jointly decomposes the user-item interaction matrix and the user-user co-occurrence matrix with shared user latent factors. Then, the learned user latent factors containing network embedding information are used as features to detect attackers. Experiments conducted on simulated and real-world datasets show that CoDetector has a good performance and generalization capacity and outperforms state-of-the-art methods.
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Acknowledgment
This research is supported by the National Key Basic Research Program of China (973) (2013CB328903), and the Graduate Scientific Research and Innovation Foundation of Chongqing (cys17035).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Dou, T., Yu, J., Xiong, Q., Gao, M., Song, Y., Fang, Q. (2018). Collaborative Shilling Detection Bridging Factorization and User Embedding. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_43
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DOI: https://doi.org/10.1007/978-3-030-00916-8_43
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