User-based collaborative filtering recommendation method combining with privacy concerns intensity in mobile commerce Online publication date: Mon, 22-Jul-2019
by Qibei Lu; Feipeng Guo; Ruoyi Zhang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 17, No. 1, 2019
Abstract: The existing personalised recommender system gives little consideration to users' privacy concerns in mobile commerce. In order to address this issue and some other shortcomings in item recommendations, the paper proposes a novel user-based collaborative filtering recommendation method combining with privacy concerns intensity and introduces the users' six dimensions privacy concerns factors, such as privacy tendency, internal control point, openness, extroversion, agreeableness, and social group influence. The paper puts forward the metric method of privacy concerns intensity with these privacy concerns influence factors, which are used to obtain the similarity preference of users for collective filtering recommendation. Experiments show that this method has more advantages than other algorithms. More importantly, a combination of subjective privacy concerns and objective recommendation technology can reduce the influence of users' privacy concerns on their acceptance of mobile personalised service.
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