Can user gender and recommendation performance be preserved simultaneously?

A recommendation system learns a user´s interests from her historical purchasing or watching behavior, which is disclosed to the recommendation system inevitably. Such a disclosure raises a serious concern in the public for the leak of users´ privacy. For instance, a person who watches a lot of videos which are more preferred by women than men can be inferred as female. Recently, as a response to this concern, some algorithms are proposed to obfuscate users´ historical behavior records to protect users´ privacy, at the cost of degradation of recommendation accuracy. It is a common belief that such degradation is inevitable. In this paper, however, we break this pessimistic belief based on the fact that a person´s interests are not necessarily limited to items which are geared to a certain age, profession, or gender. Based on this idea, we propose a recommendation-friendly privacy preserving method by introducing a privacy-preserving module between a recommendation system and user side. By obfuscating a user´s historical records with a set of properly selected extra factitious records, the privacy-preserving module can efficiently obfuscate the user´s privacy information but keep her interest information pass through. Extensive experiments show that our algorithm can not only obfuscate users´ gender information efficiently, but also maintain or even improve recommendation accuracy.