Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation

Tag recommendation has gained significant popularity for annotating various web-based resources including web services. Compared with other approaches, tag recommendation based on supervised learning models usually lead to good accuracy. However, a high-quality training data set is needed, which demands manual tagging efforts from domain experts. While we could leverage the tags of existing web services assigned by their developers, the quality of these tags may not be good enough to build accurate classifiers for tag recommendation. In this paper, a novel multi-label active learning approach is proposed for web service tag recommendation. The proposed approach is able to identify a small number of most informative web services to be tagged by domain experts. We further minimize the domain expert efforts by learning and leveraging the correlations among tags to improve the active learning process. We conduct a comprehensive experimental study on a real-world data set and results demonstrate the effectiveness of our approach.