School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel). 2018 Jun 26;18(7):2037. doi: 10.3390/s18072037.
Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.
服务推荐已成为从海量数据中快速提取有价值信息的有效方式。然而，在云环境中，用于做出推荐决策的服务质量 (QoS) 数据通常由分布式传感器进行监控，并存储在不同的云平台中。在这种情况下，在保证用户隐私的同时，整合这些来自不同平台的分布式数据（由远程传感器监控）是云环境中成功进行服务推荐的一项重要而具有挑战性的任务。局部敏感哈希 (LSH) 是实现上述数据集成和隐私保护目标的一种很有前途的方法，但当前基于 LSH 的推荐研究很少考虑可能的推荐失败情况，从而大大降低了推荐系统的稳健性。针对这一挑战，我们开发了一种新的 LSH 变体，称为反向 LSH，并基于该技术提出了一种针对推荐失败的异常处理方法。最后，我们基于著名的数据集 Movielens 进行了多次模拟实验，以证明我们方法的有效性和效率。
