Previous shilling attack detection algorithms usually deal with a specific kind of attacking strategy, and are exhausted to handle with the continuously emerging new cheating methods. However, the existence of spam user accounts in the review systems introduce unfavourable disturbances into personalized recommendation by promoting or degrading targeted items intentionally through fraudulent reviews. Besides, they compose the key component of various personalized recommender systems. The user generated reviews not only help other users to gain a more insightful view of the products, but also help online businesses to make targeted improvements on the products or services. N2 - Many e-commerce systems allow users to express their opinions towards products through user reviews systems. T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 In this work, we propose to conduct shilling attack detection for more informed recommendation by fraudulent action propagation on the reviews themselves, without caring about the specific underlying cheating strategy, which allows us a unified and flexible framework to detect the spam users.',

In this work, we propose to conduct shilling attack detection for more informed recommendation by fraudulent action propagation on the reviews themselves, without caring about the specific underlying cheating strategy, which allows us a unified and flexible framework to detect the spam users.Ībstract = 'Many e-commerce systems allow users to express their opinions towards products through user reviews systems.

Many e-commerce systems allow users to express their opinions towards products through user reviews systems.

Dark sheep attack