Mining Product Relationships for Recommendation Based on Cloud Service Data

With the rapid growth of cloud services, it is more and more difficult for users to select appropriate service. Hence, an effective service recommendation method is need to offer suggestions and selections. In this paper, we propose a two-phase approach to discover related cloud services for recommendation by jointly leveraging services' descriptive texts and their associated tags. In Phase 1, we use a non-parametric Bayesian method, DPMM to classify a large number of cloud services into an optimal number of clusters. In Phase 2, we recommend a personalized PageRank algorithm to obtain more related services for recommendation among the massive cloud service products in the same cluster. Empirical experiments on a real data set show that the proposed two-phase approach is more successful than other candidate methods for service clustering and recommendation.