Paper Info
Title
Structure Reinforcing and Attribute Weakening Network based API Recommendation Approach for Mashup Creation
Abstract
With the explosive growth of Web APIs on the Internet, it is a challenge to recommend desirable Web APIs from multiple ecosystems to develop a Mashup. Most existing API service recommendation methods focus on functional semantic similarity, but underutilize the rich network relations which inherently reflect either positive or negative relevance between services. Moreover, in the recommendation process, they usually pay too much attention to the interactions between Mashups and APIs, but ignore the cooperation between APIs. In this paper, we propose a novel method named SRAWN (Structure Reinforcing and Attribute Weakening Network) based API recommendation approach for Mashup creation. Specifically, we first design a feature extractor layer to capture structure relationship and attribute information from an API relation network graph by introducing a GAT2VEC framework, and obtain representation vectors corresponding to each API. Then, a matching evolving layer is proposed to capture the matching evolving process between APIs. At this layer, APIs are chosen incrementally to composite a Mashup, and the embedding vectors of the Mashup's existing composition features are updated adaptively based on diverse candidate APIs, by introducing a Deep Interest Network. Comprehensive experiments on a real-world dataset show that SRAWN outperforms the other state-of-the-art solutions.
Year
DOI
Venue
2020
10.1109/ICWS49710.2020.00078
2020 IEEE International Conference on Web Services (ICWS)
Keywords
DocType
ISBN
Mashup Creation,GAT2VEC framework,Deep Interest Network,API recommendation
Conference
978-1-7281-8787-7
Citations 
PageRank 
References 
0
0.34
10
Authors
7
Name
Order
Citations
PageRank
Yong Xiao100.34
Jianxun Liu264067.12
Kang, G.3295.56
Rong Hu410.70
Buqing Cao595.93
Yingcheng Cao652.17
Min Shi7114.31