Towards Efficient Pairwise Ranking for Service Using Multidimensional Classification

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.