Amplified locality‐sensitive hashing‐based recommender systems with privacy protection
Amplified locality‐sensitive hashing‐based recommender systems with privacy protection
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DOI：
10.1002/cpe.5681
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Xiaoxiao Chi;Chao Yan;Hao Wang;Wajid Rafique;Lianyong Qi
中科院分区：
文献类型：
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作者：
Xiaoxiao Chi;Chao Yan;Hao Wang;Wajid Rafique;Lianyong Qi
随着物联网时代的到来，Web服务的种类和数量都在快速增长。这通常会导致用户对Web服务的选择更加复杂。在这种情况下，各种各样的方法，如协同过滤被用来处理这一具有挑战性的情况。传统的协同过滤方法存在一些不足，其中之一是只考虑集中式的用户服务数据，而忽略了来自多个平台的分布式质量数据。跨平台的服务推荐通常涉及多个平台之间的数据通信，在此期间可能会泄露用户隐私并且需要大量的计算时间。针对这些问题，提出了一种基于局部敏感散列（LSH）的服务推荐方法，即SRAmplified-LSH。SRAmplified-LSH可以保证推荐的准确性和效率以及用户隐私信息之间的良好平衡。最后，在WS-DREAM数据集上进行了大量实验，验证了该方法的可行性.
With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.