Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering
Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering
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DOI：
10.1109/tsc.2012.31
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发表时间：
2013-10
影响因子：
8.1
通讯作者：
Huifeng Sun;Zibin Zheng;Junliang Chen;Michael R. Lyu
中科院分区：
文献类型：
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作者：
Huifeng Sun;Zibin Zheng;Junliang Chen;Michael R. Lyu
随着互联网上Web服务数量的不断增加，个性化的Web服务选择和推荐变得越来越重要。在本文中，我们提出了一种新的相似性度量Web服务的相似性计算，并提出了一种新的协同过滤方法，称为正常恢复协同过滤，个性化Web服务推荐。为了评估我们的方法的Web服务推荐性能，我们进行了大规模的真实世界的实验，涉及73个国家的5,825个真实世界的Web服务和30个国家的339个服务用户。据我们所知，我们的实验是服务计算领域最大规模的实验，比以前的记录提高了100倍。实验结果表明，我们的方法实现了更好的准确性比其他竞争的方法。
With the increasing amount of web services on the Internet, personalized web service selection and recommendation are becoming more and more important. In this paper, we present a new similarity measure for web service similarity computation and propose a novel collaborative filtering approach, called normal recovery collaborative filtering, for personalized web service recommendation. To evaluate the web service recommendation performance of our approach, we conduct large-scale real-world experiments, involving 5,825 real-world web services in 73 countries and 339 service users in 30 countries. To the best of our knowledge, our experiment is the largest scale experiment in the field of service computing, improving over the previous record by a factor of 100. The experimental results show that our approach achieves better accuracy than other competing approaches.