A Deep Recommendation Framework for Completely New Users in Mashup Creation

Abstract

When service business is in evolution from B2B to B2C model, a coldstart problem raises for service composition due to the completely new clients with no historical records. Therefore, it is of great importance to solve the cold-start problem brought by completely new users. In this paper, we propose a recommendation framework for completely new users in Mashup creation based on deeplearning technology. Firstly, this framework extracts the mapping relationship between Mashup description and APIs offline by the deep neural network. Then, when the completely new users have the Mashup demands online, the matching APIs are recommended for them by using the mapping relationship. The experimental results with real-world datasets show that our proposed model outperforms the state-of-the-art ones in term of both accuracy and recall rate. The accuracy of the proposed method is 1.34 times higher than that of the state-of-the-art methods, and the recall rate is 1.55 times higher than that of the state-of-the-art methods. Moreover, considering that the new user history invocational data is very sparse, the performance of the proposed method can be greatly improved on the denser dataset.