A COMPARATIVE STUDY FOR PRIVACY-AWARE RECOMMENDATION SYSTEMS

Abstract

Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.