A two-dimensional time-aware cloud service recommendation approach with enhanced similarity and trust

• Published in: Journal of parallel and distributed computing Vol. 190; p. 104889
• Main Authors: Tang, Chunhua, Zhao, Shuangyao, Chen, Binbin, Lu, Xiaonong, Zhang, Qiang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2024
• Subjects: Collaborative filtering; Qos prediction and service recommendation; Similarity; Temporal certainty; Time weight; Trust; Collaborative filtering; Similarity; Trust; Temporal certainty; Qos prediction and service recommendation; Time weight
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2024.104889

•Propose an integrative method to modeling the timeliness and volatility of service QoS.•Design a hybrid model to eliminate unreliable QoS and improve the recommendation quality.•Explore implicit relationships to expand the number of nearest neighbors.•Verify the effectiveness of the proposed approach based on a real-world dataset. Collaborative Filtering (CF) is one of the most successful techniques for quality-of-service (QoS) prediction and cloud service recommendation. However, individual QoS are time-sensitive and fluctuating, resulting in the QoS predicted by CF to deviate from the actual values. In addition, existing CF approaches ignore inauthentic QoS values given by untrustworthy users. To address these problems, we develop a two-dimensional time-aware and trust-aware service recommendation approach (TaTruSR). First, considering both timeliness and fluctuation of service QoS, an integrative method incorporates time weight (time dimension) and temporal certainty (QoS dimension) are proposed to determine the contribution of co-invoked services. Time weight is computed by a personalized logistic decay function to measure QoS changes by weighting the length of the time interval, while temporal certainty is defined by entropy to acquire the degree of QoS fluctuation over a period of time. Second, a set of most similar and trusted neighbors can be identified from the view of the time-aware similarity model and trust model. In models, the direct similarity and local trust are calculated based on the QoS ratings and contribution of co-invoked services to improve the prediction accuracy and eliminate unreliable QoS. The indirect similarity and global trust are estimated based on user relationship networks to alleviate the data sparsity problem. Finally, missing QoS prediction and reliable service recommendation for the active user can be achieved based on enhanced similarity and trust. A case study and experimental evaluation on real-world datasets demonstrate the practicality and accuracy of the proposed approach.