Paper Info
Title
Integrating Tag, Topic, Co-Occurrence, and Popularity to Recommend Web APIs for Mashup Creation
Abstract
With the rapid development of Web APIs, selection of the suitable Web APIs from the service repositories for users to build Mashup applications becomes more and more difficult. Even if the existing methods show significant improvements in Web API recommendation, it is still challenging to recommend similar, diverse, and relevant Web APIs with high accuracy. In this paper, we propose a novel Web API recommendation method, which integrates tag, topic, co-occurrence, and popularity factors to recommend Web APIs for Mashup creation. This method, firstly exploits the enriched tags and topics information of Mashups and Web APIs derived by the relational topic model to calculate the similarity between Web APIs and the similarity between Mashups. Secondly, it uses the invocation times and category information of Web APIs to derive their popularity. Thirdly, multi-dimensional information, such as similar Mashups, similar Web APIs, co-occurrence and popularity of Web APIs, are modeled by factorization machines to predict and recommend top-k similar, diverse, relevant Web APIs for a target Mashup. Finally, we conduct a set of experiments, and experimental results show that our approach achieves a significant improvement in terms of precision, recall, F-measure, compared with other existing methods.
Year
DOI
Venue
2017
10.1109/SCC.2017.19
2017 IEEE International Conference on Services Computing (SCC)
Keywords
Field
DocType
tag,relational topic model,popularity,factorization machines,Web APIs recommendation
Web development,Mashup,Web API,World Wide Web,Web page,Information retrieval,Computer science,Web standards,Web modeling,Social Semantic Web,Web service
Conference
ISBN
Citations 
PageRank 
978-1-5386-2006-9
0
0.34
References 
Authors
16
6
Name
Order
Citations
PageRank
Hongchao Li1234.20
Jianxun Liu264067.12
Buqing Cao320023.96
Tang Mingdong455739.35
Xiaoqing (Frank) Liu535427.76
Bing Li638040.45