Abstract
In recent years, the research on shilling attacks has been greatly improved. However, some serious problem in hand such as attack model dependency and high computational cost. Such recommender system also provides an impressive way to overcome information overload problem. In order to preserve the trust of recommender system, it is required to identify and remove the fictitious profiles from the system. Here, we have used machine learning classifiers to detect the attacker’s profiles. A new model is proposed that outperforms in most of the cases.
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Verma, A.K., Dixit, V.S. (2019). A Comparative Evaluation of Profile Injection Attacks. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences . Lecture Notes in Networks and Systems, vol 39. Springer, Singapore. https://doi.org/10.1007/978-981-13-0277-0_4
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DOI: https://doi.org/10.1007/978-981-13-0277-0_4
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