实验室教师一篇论文被IEEE Transactions on Services Computing录用
发布时间：2017/3/21 4:51:39 阅读次数：1130
近日，实验室教师的一篇学术论文“Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development”被IEEE Transactions on Services Computing（TSC）期刊收录为Regular论文。IEEE Transactions on Service Computing系服务计算领域国际顶级期刊，目前为JCR 1区、CCF B类期刊，涉及的研究方向主要包括服务计算、云计算以及面向服务的软件工程等。所发表论文简要信息如下：
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标题: Integrated Content and Network-Based Service Clustering and Web APIs Recommendation for Mashup Development
作者: Buqing Cao, Xiaoqing (Frank) Liu, MD Mahfuzer Rahman, Bing Li, Jianxun Liu, Mingdong Tang
来源出版物: IEEE Transactions on Services Computing
摘要：The rapid growth in the number and diversity of Web APIs, coupled with the myriad of functionally similar Web APIs, makes it difficult to find most suitable Web APIs for users to accelerate and accomplish Mashup development. Even if the existing methods show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. In this paper, we propose an integrated content and network-based service clustering and Web APIs recommendation method for Mashup development. This method, first develop a two-level topic model by using the relationship among Mashup services to mine the latent useful and novel topics for better service clustering accuracy. Moreover, based on the clustering results of Mashups, it designs a collaborative filtering (CF) based Web APIs recommendation algorithm. This algorithm, exploits the implicit co-invocation relationship between Web APIs inferred from the historical invocation history between Mashups clusters and the corresponding Web APIs, to recommend diverse Web APIs for each Mashups clusters. The method is expected to not only find much better matched Mashups with high accuracy, but also diversify the recommendation result of Web APIs with full coverage. Finally, based on a real-world dataset from ProgrammableWeb, we conduct a comprehensive evaluation to measure the performance of our method. Compared with existing methods, experimental results show that our method significantly improves the accuracy and diversity of recommendation results in terms of precision, recall, purity, entropy, DCG and HMD.
（编辑 肖巧翔）
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