Opinion mining-based secured collaborative recommender system
by Veer Sain Dixit; Akanksha Bansal Chopra
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 10, No. 4, 2021

Abstract: Recommender systems have impressively hit the e-commerce industry in the last few years. With the increased use of recommender systems, the risk of preventing the authentic and true data has also been increased. Many algorithms have been proposed by various researchers to detect and prevent an attack, in recent years. But presently, no algorithm exists as a complete solution to this issue because of one or other constraints. The research in the paper intends to provide a solution to this issue and proposes a framework of more secured collaborative recommender system as compared to conventional recommender systems. The proposed framework uses the approach of Turing test to identify any robotic involvement and does not allow insertion of ratings from such non-human users. Ratings from real users are allowed, collected and examined. Textual opinions are also collected while collecting the ratings. Opinion mining is performed on given textual opinion to identify and discard push and nuke ratings. Valid ratings are stored in the database for generating recommendations for items. The paper also examines the effect of push and nuke ratings on performance by evaluating the accuracy of a recommender system using various measure metrics.

Online publication date: Mon, 24-Jan-2022

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