Mashup tag completion with attention-based topic model

The past few years have witnessed a substantial increase in functional rich API services and their compositions (e.g., Mashup services) on the Internet, which as a result proposes new requirement of organization and management methods for better service understanding, discovery and usage. Tagging is known to be efficient for this purpose, and many tag recommender systems for API services have been proposed in the past. Existing approaches targeted at API services usually recommend several similar tags, which is insufficient for Mashup services that normally demonstrate diverse functionalities. In this paper, we propose a novel approach for Mashup service tag completion that can recommend tags revealing the functional features of Mashup services more comprehensively. It first extracts candidate tags for the target Mashup from API services in different functional domains. Then, it adopts a hybrid filtering procedure to recommend the most relevant tags. To support efficient tag extraction and recommendation, an attention-based topic model called Att-LDA is proposed that can highlight the functional oriented features in descriptions of services for accurate functional semantic learning. Experiments and validations on a real-word dataset demonstrate the performance of our approach improved 7.1% compared with other state-of-the-art methods.