PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

Abstract Web API is a popular way to organize network services in cloud computing environment. However, it is a challenge to find an appropriate service for the requestor from massive Web API services. Service clustering can improve the efficiency of service discovery for its ability of reducing search space. Latent Dirichlet Allocation (LDA) is the most frequently used topic model in service clustering. To further improve the topic representation ability of LDA, we propose a new variant model of LDA with probability incremental correction factor (PICF-LDA) to generate the high-quality service representation vectors (SRVs) for Web API services. We first compute the words’ topic contribution degree (TCD) in the service description text by its context weight and part-of-speech (POS) weight. Then the probability incremental correction factor (PICF) for a word is designed based on TCD and the word’s maximum topic probability value. PICF is used to correct the probability distributions in SRVs. Experiments show that PICF-LDA has a better performance than LDA, the variant LDA models and other state-of-the-art topic models in service clustering