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.
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