A privacy-preserving recommendation method based on multi-objective optimisation for mobile users
Chonghuan Xu, Austin Shijun Ding, Stephen Shaoyi Liao
Abstract
Recommender systems have proven to be an effective technique to deal with information overload and mislead problems by helping users get useful and valuable information or objects from massive data. However, exploiting users' preferences with recommendation algorithms lead to serious privacy risks, especially when recommender service providers are unreliable. An ideal recommender system should be both accurate, diverse and security. In this paper, we propose a private recommendation method which consists of a private collaborative filtering algorithm and a multi-objective evolutionary algorithm for mobile users. Experimental results demonstrate that even though the mobile users' preferences are significantly obfuscated, our method is effective in terms of recommendation accuracy and diversity.