SeCo-LDA: Mining Service Co-occurrence Topics for Recommendation

Abstract

Service ecosystem consists of all kinds of services, and some of them may be composed by developers to create new mashups. Existing work on service recommendation and composition mine either frequent patterns from mashup-service usage records, or latent topics from service metadata. In this paper, we propose Service Co-occurrence LDA (SeCo-LDA), a novel approach that mines latent topic models over service co-occurrence patterns. The key idea is to treat each service as a document, and its bag of co-occurring services as the bag of words in that document. Using this model, we can analyze such service co-occurrence documents with a probabilistic topic model. We show how to derive service co-occurrence topics, and then validate our model on the real-world ProgrammableWeb.com dataset. We illustrate that SeCo-LDA can discover meaningful latent service composition patterns including their temporal strength and services' impacts, which conventional Apriori can not reveal. Comparing with Apriori, content matching based on service description and LDA directly using mashup-service usage records, we have demonstrated that SeCo-LDA can recommend service composition more effectively, 5% better in terms of MAP than the baseline approach.