A HYBRID APPROACH FOR AUTOMATIC MASHUP TAG RECOMMENDATION
Tags have been extensively utilized to annotate Web services, which is beneficial to the management, classification and retrieval of Web service data. In the past, a plenty of work have been done on tag recommendation for Web services and their compositions (e.g. mashups). Most of them mainly exploit tag service matrix and textual content of Web services. In the real world, multiple relationships could be mined from the tagging systems, such as composition relationships between mashups and Application Programming Interfaces (APIs), and co-occurrence relationships between APIs. These auxiliary information could be utilized to enhance the current tag recommendation approaches, especially when the tag service matrix is sparse and in the absence of textual content of Web services. In this paper, we propose a hybrid approach for mashup tag recommendation. Our hybrid approach consists of two continuous processes: APIs selection and tags ranking. We first select the most important APIs of a new mashup based on a probabilistic topic model and a weighted PageRank algorithm. The topic model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to elicit the latent topic information. Then, tags of chosen important APIs are recommended to this mashup. In this process, a tag filtering algorithm has been employed to further select the most relevant and prevalent tags. The experimental results on a real world dataset prove that our approach outperforms several state-of-the-art methods.
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