Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation
Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation
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DOI：
10.1007/s10115-012-0562-1
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发表时间：
2012-10
影响因子：
2.7
通讯作者：
Jie Cao;Zhiang Wu;Youquan Wang;Zhuang Yi
中科院分区：
文献类型：
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作者：
Jie Cao;Zhiang Wu;Youquan Wang;Zhuang Yi
Web服务推荐已成为服务计算领域的一个热点而又基础的研究课题。最流行的技术是基于用户项矩阵的协同过滤（CF）。但是，它不能很好地捕获Web服务和提供者之间的关系。为了解决这个问题，我们首先设计一个多维数据集模型来显式地描述提供者、消费者和Web服务之间的关系。然后，我们提出了基于标准偏差的Web服务推荐混合协同过滤（SD-HCF）和基于逆消费者频率的潜在消费者推荐用户协同过滤（IF-UCF）。最后，给出了双向推荐的决策过程，包括提供者和消费者。在Planet-Lab提供的真实世界数据上进行了一系列实验。在实验阶段，我们展示了SD-HCF的参数对预测质量的影响，并证明了SD-HCF在推荐质量上远远优于现有的方法，包括基于用户的推荐、基于项目的推荐和一般HCF。对IF-UCF和UCF的实验比较表明，在UCF中加入逆消费者频率是有效的。
Web service recommendation has become a hot yet fundamental research topic in service computing. The most popular technique is the Collaborative Filtering (CF) based on a user-item matrix. However, it cannot well capture the relationship between Web services and providers. To address this issue, we first design a cube model to explicitly describe the relationship among providers, consumers and Web services. And then, we present a Standard Deviation based Hybrid Collaborative Filtering (SD-HCF) for Web Service Recommendation (WSRec) and an Inverse consumer Frequency based User Collaborative Filtering (IF-UCF) for Potential Consumers Recommendation (PCRec). Finally, the decision-making process of bidirectional recommendation is provided for both providers and consumers. Sets of experiments are conducted on real-world data provided by Planet-Lab. In the experiment phase, we show how the parameters of SD-HCF impact on the prediction quality as well as demonstrate that the SD-HCF is much better than extant methods on recommendation quality, including the CF based on user, the CF based on item and general HCF. Experimental comparison between IF-UCF and UCF indicates the effectiveness of adding inverse consumer frequency to UCF.