基于BC聚类的差分隐私保护推荐算法

为提高差分隐私保护下推荐算法的准确性，提出了一种考虑差分隐私保护的基于Bhattacharyya系数（BC）的聚类推荐算法.以BC作为项目相似性度量的标准，根据BC相似性对项目进行K-medoids聚类，并在聚类簇中进行私有项目邻居选择.最后，根据最近邻居集信息，对用户的评分进行预测和Top-n推荐.提出的方案有效地克服了已有方法中存在的相似性度量依赖于共同评分的问题，提高了相似性度量的准确性，有效避免了因隐私保护而造成的最近邻居集质量下降的问题.理论分析和实验测试的结果表明，该方法在实现隐私保护的同时还能有效保证推荐的高质量，较好地实现了隐私保护和数据效用之间的平衡，具有良好的应用潜力.

Differential Privacy-Preserving Recommendation Algorithm Based on Bhattacharyya Coefficient Clustering

To improve the accuracy of recommendation algorithm under differential privacy protection, a privacy preservation recommendation algorithm is proposed based on a clustering method with Bhattacharyya coefficient(BC). In the proposed algorithm, the Bhattacharyya coefficient is used as the standard of measuring item similarity. Based on the BC similarity, the items are clustered by K-medoids, and the private neighbors of the items are selected from the clusters. Finally, according to the selected nearest neighbor set, the user's rating is predicted and the Top-n recommendations are output. The proposed algorithm effectively overcomes the problem that the calculation of similarity must depend on the common rated ratings, improves the accuracy of the similarity measurement, and also avoid the problem of quality degradation of the nearest neighbor set due to privacy protection. It is shown that the proposed algorithm not only achieves privacy preservation but also guarantees the high quality of recommendation. Therefore, the proposed algorithm effectively balances the privacy preservation and the data utility, which has good application potential in the recommendation system.