Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation
Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation
复制标题
具有隐私保护的时间感知且稀疏容忍的混合协作推荐方法
DOI：
10.1002/cpe.5447
复制
发表时间：
2020
影响因子：
2
通讯作者：
Dou Wanchun
中科院分区：
文献类型：
--
作者：
Meng Shunmei;Li Qianmu;Zhang Jing（张静）;Lin Wenmin;Dou Wanchun
随着云服务的爆炸式增长，如何设计有效的推荐模型变得越来越重要。时间信息已被证明是影响推荐性能的重要因素。实际上，用户行为和服务的QoS性能都具有时间敏感性，特别是在动态云环境中。然而，大多数现有的协同推荐方法很少考虑时间对QoS性能的影响。此外，随着云中的安全威胁越来越多，隐私保护成为推荐系统需要解决的一个重要问题。基于这些观察结果，本文提出了一种具有时间感知和稀疏度容忍的隐私保护混合协同推荐方法，该方法将基于张量分解的CF模型与基于邻域的CF模型相结合，以提高推荐性能。具体来说，通过区分短期QoS指标和长期QoS指标，在推荐模型中考虑了时间影响。采用隐私感知的时间聚合机制，保护用户的敏感时间信息。此外，为了解决稀疏性问题，提出了一种基于张量分解技术的面向稀疏性的数据QoS评级预测机制，用于预测缺失或未评级的QoS评级数据。最后，根据稳定近邻和时间近邻进行预测。通过实验验证了该方法的预测性能。
With the explosive growth of cloud services, how to design effective recommendation models has become more and more important. Temporal information has been proved to be an important factor affecting recommendation performance. Actually, both of user behaviors and QoS performance of services are time‐sensitive, especially in dynamic cloud environment. However, most existing collaborative recommendation methods seldom consider temporal influence to QoS performance. Furthermore, with the ever‐increasing number of security threats in clouds, privacy preservation becomes an important problem to be addressed in recommender systems. Based on these observations, in this paper, we propose a temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation, where tensor factorization‐based CF model is integrated into neighborhood‐based CF model to achieve improved recommendation performance. Specifically, temporal influence is considered into recommendation model by distinguishing short‐term QoS metrics from long‐term QoS metrics. A privacy‐aware time aggregation mechanism is adopted to preserve the sensitive time information of users. Moreover, to deal with the sparsity problem, a sparsity‐oriented data QoS rating prediction mechanism based on tensor factorization technique is applied to predict the missing or unrated QoS rating data. Finally, predictions are made based on both stable and temporal nearest neighbors. Experiments are conducted to demonstrate the prediction performance of our proposal.