End-to-End Web Service Recommendations by Extending Collaborative Topic Regression

Abstract

Mashup has emerged as a lightweight way to compose multiple web services and create value-added compositions. Facing the large amount of services, effective service recommendations are in great need. Service recommendations for mashup queries suffers from a mashup-side cold-start problem, and traditional approaches usually overcome this by first applying topic models to mine topic proportions of services and mashup queries, and then using them for subsequent recommendations. This solution overlooks the fact that usage record can provide a feedback for text extraction. Besides, traditional approaches usually treat all the usage records equally, and overlook the fact that the service usage pattern is evolving. In this article, the authors overcome these issues and propose an end-to-end service recommendation algorithm by extending collaborative topic regression. The result is a generative process to model the whole procedure of service selection; thus, usage can guide the mining of text content, and meanwhile, they give time-aware confidence levels to different historical usages. Experiments on the real-world ProgrammableWeb data set show that the proposed algorithm gains an improvement of 6.3% in terms of mAP@50 and 10.6% in terms of Recall@50 compared with the state-of-the-art methods.