Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems
Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems
复制标题
协作推荐系统的隐私保护和稀疏感知的基于位置的预测方法
DOI：
10.1016/j.future.2019.02.016
复制
发表时间：
2019
影响因子：
7.5
通讯作者：
Shaohua Wan
中科院分区：
文献类型：
--
作者：
Shunmei Meng;Lianyong Qi;Qianmu Li;Wenmin Lin;Xiaolong Xu;Shaohua Wan
随着公有云产品的快速增长，如何设计有效的预测模型，为潜在用户提供合适的推荐变得越来越重要。在动态云环境中，用户行为和服务性能都对地理位置信息等上下文信息非常敏感。此外，日益增多的攻击和安全威胁也带来了如何更有效、更安全地保护敏感数据、云资源和通信等关键信息资产的问题。针对这些挑战，我们提出了一种基于位置预测的隐私保护和稀疏感知的协同推荐系统方法。具体地说，我们的方法分为三个阶段：首先，提出了两种隐私保护机制，即随机数据混淆技术和区域聚合策略，以保护用户的隐私信息并解决数据稀疏性问题。然后，应用基于张量分解的位置感知潜在因素模型来研究服务之间的空间相似性关系。最后，基于全局近邻和空间近邻进行预测。设计并进行了实验，验证了该方案的有效性。实验结果表明，该方法在保证隐私保护的前提下，取得了较好的预测精度。
With the rapid growth of public cloud offerings, how to design effective prediction models that provide appropriate recommendations for potential users has become more and more important. In dynamic cloud environment, both of user behaviors and service performance are sensitive to contextual information, such as geographic location information. In addition, the increasing number of attacks and security threats also brought the problem that how to protect critical information assets such as sensitive data, cloud resources and communication in a more effective and secure manner. In view of these challenges, we propose a privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Specifically, our method is designed as a three-phase process: Firstly, two privacy-preserving mechanisms, i.e., a randomized data obfuscation technique and a region aggregation strategy are presented to protect the private information of users and deal with the data sparsity problem. Then a location-aware latent factor model based on tensor factorization is applied to explore the spatial similarity relationships between services. Finally, predictions are made based on both global and spatial nearest neighbors. Experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method achieves decent prediction accuracy on the premise of privacy preservation.