Abstract
As the number of cloud services and user interest data soars, it’s hard for users to find suitable could services within a short time. A suitable cloud service automatic recommendation system can effectively solve this problem. In this work, we propose KGCF, a novel method to recommend users cloud services that meet their needs. We model user-item and item-item bipartite relations in a knowledge graph, and study property-specific user-item relation features from it, which are fed to a collaborative filtering algorithm for Top-N item recommendation. We evaluate the proposed method in terms of Top-N recommendation on the MovieLens 1M dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation, which means that more kinds cloud services can be recommended to users instead of only hot items.
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Acknowledgment
This paper is supported by the National Science Youth Foundation of China under Grant No. 61702264, the Fundamental Research Funds for the Central Universities No. 30919011282, the Fundamental Research Funds for the Central Universities No. 30918014108, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China No. 19KJB510022.
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Huang, W., Li, Q., Liu, X., Meng, S. (2020). Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_21
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