Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings

Research Article

Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification

  • @INPROCEEDINGS{10.1007/978-3-030-30146-0_44,
        author={Yingying Yuan and Jiwei Huang and Yeping Zhu and Yufei Hu},
        title={Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={8},
        keywords={Service ranking Multidimensional classification Pairwise ranking Markov model},
        doi={10.1007/978-3-030-30146-0_44}
    }
    
  • Yingying Yuan
    Jiwei Huang
    Yeping Zhu
    Yufei Hu
    Year: 2019
    Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-30146-0_44
Yingying Yuan1,*, Jiwei Huang2,*, Yeping Zhu3,*, Yufei Hu4,*
  • 1: Beijing University of Posts and Telecommunications
  • 2: China University of Petroleum - Beijing
  • 3: Chinese Academy of Agricultural Sciences
  • 4: Beijing Boyu Kaixin Machinery Equipment Co., Ltd.
*Contact email: yuanyingying@bupt.edu.cn, huangjw@cup.edu.cn, zhuyeping@caas.cn, yufeihu1996@outlook.com

Abstract

With the growing popularity of services which meet the divergent requirements from users, service selection and recommendation have drawn significant attention in services computing community. Service ranking is the most important part in service selection and recommendation. Although there have been several existing approaches of service ranking which is basically rating-based, suffering from the heterogeneity of ranking criteria from users. Moreover, the efficiency of such comparison-based approaches is the bottleneck in reality. To attack these challenges, an efficient pairwise ranking scheme with multidimensional classification is proposed in this paper, which also fully considers the context information of service and users. Furthermore, the scheme is able to mitigate data sparsity of users similarity matrix and improve accuracy. Next, we introduce a random walk model for ranking formulation, and propose a Markov chain based approach to obtain the global ranking. Finally, the efficacy of our approach is validated by experiments adopting the real-world YELP dataset.