Purpose – Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They utilize recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information in order to provide relevant recommendations. However due to privacy concerns users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable.udDesign/methodology/approach – This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method have been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware dataset has been used to run performance and usability tests.udFindings – The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance user privacy can be protected.udOriginality/value – This is a novel research paper that proposes a method for protecting the privacy of mobile recommender systems users when context parameters are used.
展开▼
