Recommending Services for New Mashups through Service Factors and Top-K Neighbors

One of the most interesting research directions in service computing is to leverage current recommendation system solutions to suggest web services for a mashup application. Existing approaches are mainly based on collaborative filtering techniques, which can suffer from the heavy rely on human input, data sparsity and cold start issues, resulting in low accuracy. In this paper, we leverage advanced probabilistic model based approaches to tackle these issues. Our solution is to make service recommendation based on the service features and historical usage. We use the Hierarchical Dirichlet Process (HDP), a nonparametric Bayesian approach to intelligently discover the functionally relevant services based on their specifications. We leverage Probabilistic Matrix Factorization (PMF) to recommend services based on historical usage and tackle the cold start issues for new mashups through their top-K neighbors. We integrate the suggesting results from these two approaches through the Bayesian theorem and take the indicator of quality of service into account to make the final suggestion. We compared our approach with some existing approaches using a real world data set and the result indicates that our approach performs the best.