Privacy Recommendation Based on Bhattacharyya Coefficient

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In the era of big data, how to achieve the trade-off between recommendation accuracy and privacy protection has become a hot topic in the field of recommendation. In this work, a recommendation scheme with personal privacy preservation is designed. Besides, it does not depend on co-ratings. In order to solve the problem of excessive sensitivity when the Laplace noise is introduced directly, the Bhattacharyya coefficient that is used as the similarity metric is normalized. Moreover, a personalized privacy protection scheme that considering the difference of users is used for the recommendation system. Experimental results show that, compared with the traditional differential privacy collaborative filtering scheme, the RMSE and MAE of the proposed scheme are significantly improved. The proposed scheme not only ensures the accuracy of the prediction results but also effectively guarantees the privacy protection of user data. It also provides an insight into the design of recommendation services considering privacy protection.