LMA: label-based multi-head attentive model for long-tail web service classification

Abstract

With the rapid growth of web services, service classification is widely used to facilitate service discovery, selection, composition and recommendation. Although there is much research in service classification, work rarely focuses on the long-tail problem to improve the accuracy of those categories which have fewer services. In this paper, we propose a novel label-based attentive model LMA with the multi-head structure for long-tail service classification. It can learn the various word-label subspace attention with a multi-head mechanism, and concatenate them to get the high-level feature of services. To demonstrate the effectiveness of LMA, extensive experiments are conducted on 14,616 real-world services with 80 categories crawled from the service repository ProgrammableWeb. The results prove that the LMA outperforms state-of-the-art approaches for long-tail service classification in terms of multiple evaluation metrics.