A Topic-Sensitive Method for Mashup Tag Recommendation Utilizing Multi-Relational Service Data

Tagging systems have been widely used as a major way of managing Web service resources. Many portals such as ProgrammableWeb and BioCatalogue allow users to create manual tags annotating Web services and their compositions (e.g., mashups). This is extremely helpful for managing and retrieving enormous Web service data. In the past few years, many tag recommendation approaches have been proposed for Web services that contain few or no tags. Most of them only exploit the textual content or tag service matrix information. Sometimes those approaches suffer from the data sparsity problem, especially when Web services have only few tags or their auxiliary textual contents are hard to be obtained. In real world, a plenty of relationships are available in recommendation systems, e.g., the composition relationship between services and the annotation relationship between mashups and tags. These multi-relational data can be utilized as additional features to improve the recommendation performance. In this paper, we exploit various types of relationships as features and propose a novel topic-sensitive approach based on the Factorization Machines for mashup tag recommendation. Factorization Machines is utilized to model the pair-wise interactions between all features and predict adequate tags for mashups. In this approach, we first obtain the latent topics of all tags as well as the description documents for mashups and APIs based on a novel probabilistic topic model. Then, a multi-relational network by mining various relationships from the Web service data is constructed. Various auxiliary informations are subsequently extracted from the network to train the Factorization Machines. The proposed model is evaluated on three real-world datasets and the experimental results show that it outperforms several state-of-the-art methods.

Automated summary

This paper presents a topic-sensitive approach for mashup tag recommendation, utilizing various relationship types and Factorization Machines to enhance recommendation performance. Experimental results demonstrate that this method outperforms several state-of-the-art approaches.