摘要:如何根据用户的自然语言需求描述自动生成或推荐用于解决问题的Web API服务集合，并辅助构建Mashup，是业务流程管理者和服务组合者关注的热点之一.如何提高推荐的质量，是大家关注的焦点.为此，提出了一种融合多维信息的主题自适应Web API推荐方法HDP-FM（hierarchical Dirichlet processes-factorization machines）为Mashup的创建推荐Web APIs集合.该方法以Web API的描述文档为语料库，利用HDP模型训练每个Web API的主题分布向量；其次，利用已生成的主题模型预测每个Mashup的主题分布向量，用于相似度的计算；最后，将Mashup之间的相似度、WebAPI之间的相似度、Web API的流行度和共现性作为因子分解机模型的输入，评分排序获取用于推荐的Web APIs集合.为了验证HDP-FM方法的性能，使用从ProgrammableWeb平台上爬取的真实数据进行多组实验，实验结果表明，HDP-FM方法在准确率、召回率、F-measure和NDCG@N等方面具有较好的性能.



Abstract:How to automatically generate or recommend a set of Web APIs for Mashup creation according a user's natural language description of requirement is a focus of attention among business process managers and services composition designers. A topic adaptive Web API recommendation method, HDP-FM (hierarchical Dirichlet processes-factorization machine), is proposed in this paper to recommend a set of Web APIs for Mashup creation. This approach firstly makes the Web API description document as a corpus, and trains a topic distribution vector for a Web API by the HDP model. It then predicts a topic distribution vector for a Mashup via the generated model, where the topic distribution vector is used to calculate the similarity. Finally, a factorization model is utilized to score and sort Web APIs by taking the similarity between Mashups, the similarity between Web APIs, the popularity of Web APIs and the co-occurrence of Web APIs as inputs. A Mashup can be created based on these recommended Web APIs. To verify the performance of the HDP-FM method, a series of experiments are conducted on a real dataset crawled from the ProgrammableWeb platform. The results show that the HDP-FM method has a good performance over others in term of precision, recall, F-measure and NDCG@N.



PDF全文下载地址:

http://jos.org.cn/jos/article/pdf/5482