National Natural Science Foundation of China, Grant/Award Number: 62162046; Research and Application of Key Technology of Big Data for Discipline Inspection and Supervision, Grant/Award Number: 2019GG372; The Major Project of Inner Mongolia Natural Science Foundation, Grant/Award Number: 2019ZD15
Concurrency and Computation: Practice and Experience
Cloud Computing Services, Cloud Recommender Systems
Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.
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Qi, R. D., Zhou, J. T., Wang, Z., & Song, X. (2022). An elastic recommender process for cloud service recommendation scalability. Concurrency and Computation: Practice and Experience, e7066.