Learning to Reuse User Inputs in Service Composition

Abstract

Users visit web services and compose them to accomplish on-line tasks. Normally, users enter the same information into various web services to finish such tasks. However, repetitively typing the same information into services is unnecessary and decreases the service composition efficiency. In this paper, we propose a context-aware ranking approach to recommend previous user inputs into input parameters and save users from repetitive typing. We develop five different ranking features constructed from various types of information, such as user contexts. We adopt a learning-to-rank approach, a machine learning technology automatically constructing the ranking model, and integrate our ranking features into a state-of-the-art learning-to-rank framework. Our approach learns the information of interactions between input parameters and user inputs to reuse user inputs under different contexts. Through an empirical study on 960 real services, our approach outperforms two baseline approaches on ranking values to input parameters of composed services. Moreover, we observe that textual information affects the ranking most and the contextual information of location matters the most to ranking among various types of contextual data.