Manifold-Learning Based API Recommendation for Mashup Creation
Manifold-Learning Based API Recommendation for Mashup Creation
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DOI：
10.1109/icws.2015.64
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Wei Gao;Liang Chen;Jian Wu;Honghao Gao
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文献类型：
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作者：
Wei Gao;Liang Chen;Jian Wu;Honghao Gao
随着面向服务的体系结构（SOA）的广泛采用，Web可访问服务及其组合的数量正在迅速增加。在众多的服务中，如何推荐合适的服务进行自动组合以满足用户的需求是一个挑战。我们调查的服务和他们的组合在可编程Web的特点服务作为API和他们的组合作为混搭。我们研究的问题，推荐合适的API满足用户的需求混搭创作。为此，我们提出了一个多方面的排名框架API的建议。首先，我们将现有的mashup分类为功能相似的集群。然后，我们使用流形排序算法为每个mashup集群推荐API，该算法结合了mashup之间，API之间以及mashup和API之间的关系。直观地说，我们考虑三个因素：（1）我们推荐功能相似的mashup中的API。(2)我们推荐mashup中流行的API。(3)我们推荐彼此相似的API。最后，我们将用户的混搭创建需求映射到混搭集群，并向用户推荐由算法生成的API。基于Programmble Web上的真实的数据集的实验结果表明，该方法在查准率、查全率和NDCG方面都是有效的.
With the wide adoption of Service-Oriented Architecture (SOA), the number of web accessible services and their compositions is increasing rapidly. Among huge number of services, how to recommend appropriate ones for automatic composition satisfying users' need is challenging. We investigate services and their compositions in Programmable Web which characterize services as APIs and their compositions as mashups. We study the problem of recommending suitable APIs satisfying users' need for mash up creation. To this end, we propose a manifold ranking framework for API recommendation. First, we categorize existing mashups into functionally similar clusters. Then we recommend APIs for each mash up cluster using manifold ranking algorithm which incorporate the relationships between mashups, between APIs and between mashups and APIs. Intuitively, we take three factors into consideration: (1) We recommend APIs that are in functionally similar mashups. (2) We recommend APIs that are popular in the mashups. (3) We recommend APIs that are similar to each other. Finally, we map a user's requirement for mash up creation to a mash up cluster and recommend APIs generated by the algorithm to user. Experiments based on real dataset crawled from Programmble Web demonstrate the effectiveness of the proposed approach in terms of precision, recall, and NDCG.