论文成果

Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification

摘要: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.
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

ISSN号:1867-8211

卷、期、页:v 292,p654-666

发表日期:2019-01-01

期刊分区(SCI为中科院分区):无

收录情况:EI(工程索引)

发表期刊名称:Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

参与作者:袁迎迎,诸叶平,胡宇飞

第一作者:黄霁崴

论文类型:会议论文

论文概要:袁迎迎,黄霁崴,诸叶平,胡宇飞,Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification,Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST,2019,v 292,p654-666

论文题目:Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification