Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation. (25th July 2019)

• Record Type: Journal Article
• Authors: Meng, Shunmei; Li, Qianmu; Zhang, Jing; Lin, Wenmin; Dou, Wanchun
• Is Part Of: Concurrency and computation. Volume 32:Number 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-25
• Subjects: collaborative filtering -- privacy preservation -- recommendation -- sparsity -- temporal; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5447
• Languages: English
• ISSNs: 1532-0626

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.