Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Abstract

Summary 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.

LSH-based missing value prediction for abnormal traffic sensors with privacy protection in edge computing

“…Next, we measure the performances of our proposed AS M V P distr−L S H method and compare it with another existing methods: Ser Rec distri−L S H [44] and Optimal − Pub [45]. The recruited dataset is WS-DREAM [48].…”

“…Qi et al [44] put forward an LSH-based service recommendation approach Ser Rec distr−L S H to secure the sensitive information of users hidden in historical user ratings. However, the authors do not take the time context factor into consideration and neglect the dynamic influence of time [45] propose an "optimal publishing" strategy to reveal only the optimal service quality records instead of publishing all the sensitive service quality data observed by users. This way, most private information contained in service quality data can be protected well.…”

Security-Driven hybrid collaborative recommendation method for cloud-based iot services

“…Besides of the MF scheme, high-order tensor factorization scheme is also applied in CF recommendation algorithms to mine deeper relationships among users, items and other factors. In our previous studies [5,38], CP (Canonical Polyadic) decomposition model is adopted to mine the relationships among users, items, time and location information for contextaware recommendation. Wang et al [39] introduce a tensor-based big-data-driven routing recommendation model where a tensor matching approach integrates the controlling tensor, seed tensor and orchestration tensor for efficient routing paths recommendation.…”

“…To validate the effectiveness of SHCR in recommendation accuracy, three widely used evaluation metrics, i.e., Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), Precision and Recall [38] are applied.…”