Incorporating User, Topic, and Service Related Latent Factors into Web Service Recommendation

Due to the large and increasing number of web services, it is very helpful to provide a proactive feed on what is available to users, i.e., recommending web services. As collaborative filtering (CF) is an effective recommendation method by capturing latent factors, it has been used for service recommendation as well. However, the majority of current CF-based service recommendation approaches predict users' interests through the historical usage data, but not the service description. This makes them suitable for making QoS-based recommendation, but not for functionality-based recommendation. In this paper, we propose to use machine learning approaches to recommend web services to users from both historical usage data and service descriptions. Considering the great popularity of RESTful services, our approach is applicable to both structured and unstructured service description, i.e., free text descriptions. We exploit the idea of collaborative topic regression, which combines both probabilistic matrix factorization and probabilistic topic modeling, to form user-related, service-related, and topic related latent factor models and use them to predict user interests. We extracted public web service data and developer invocation history from ProgrammableWeb and conducted a comprehensive experiment study. The result indicates that this approach is effective and outperforms other representative recommendation methods.