DETECTION OF SHILLING ATTACKS IN RECOMMENDER SYSTEMS

Recommender Systems are widely used decision making tool in e-commerce industry.  They help users to select for interested product from the pool of products. With an open nature, Recommender systems are vulnerable to shilling attacks. This book discusses the attacks in Recommender Systems. The proposed models demonstrate the new techniques of integrating deep learning approaches with opinion mining approach and optimization algorithms. The proposed models are implemented on variety of datasets. This is because; any designed model must be flexible to be implemented on any type of dataset, as every dataset has its own features. To conclude, depending upon the nature of dataset, out of the proposed models, the best suited model can be chosen and applied to detect the attacks. The application of the work done in the e-commerce industry would yield good quality recommendations for the target product. This in turn, would benefit the users to select the correct product from the available online pool of products. The companies would be able to study the market trend more precisely, which is a key factor for their growth. The author expresses her deep indebtedness to the readers and invites suggestions for the improvement of the book.

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