Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019

Research Article

Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services

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  • @INPROCEEDINGS{10.1007/978-3-030-48513-9_21,
        author={Weijia Huang and Qianmu Li and Xiaoqian Liu and Shunmei Meng},
        title={Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services},
        proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019},
        proceedings_a={CLOUDCOMP},
        year={2020},
        month={6},
        keywords={Cloud services Recommendation systems Knowledge graph Collaborative filtering},
        doi={10.1007/978-3-030-48513-9_21}
    }
    
  • Weijia Huang
    Qianmu Li
    Xiaoqian Liu
    Shunmei Meng
    Year: 2020
    Collaborative Recommendation Method Based on Knowledge Graph for Cloud Services
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-030-48513-9_21
Weijia Huang1,*, Qianmu Li1,*, Xiaoqian Liu2,*, Shunmei Meng1,*
  • 1: Nanjing University of Science and Technology
  • 2: Jiangsu Police Institute
*Contact email: weijia@njust.edu.cn, qianmu@njust.edu.cn, liuxiaoqian@jspi.edu.cn, mengshunmei@njust.edu.cn

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.