Web service recommendation based on location clustering and tensor decomposition

Computer Engineering and Applications ›› 2016, Vol. 52 ›› Issue (15): 65-72.

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Web service recommendation based on location clustering and tensor decomposition

TANG Ni1，2, XIONG Qingyu1，2, WANG Xibin1，2, GAO Min1，2, WEN Junhao1，2, ZENG Jun1，2

1.Key Laboratory of Dependable Service Computing in Cyber Physical Society（Chongqing University）, Ministry of Education, Chongqing 400030, China
2.School of Software Engineering, Chongqing University, Chongqing 401331, China

Online:2016-08-01 Published:2016-08-12

基于位置聚类和张量分解的Web服务推荐算法

唐妮1，2，熊庆宇1，2，王喜宾1，2，高旻1，2，文俊浩1，2，曾骏1，2

1.信息物理社会可信服务计算教育部重点实验室（重庆大学），重庆 400030
2.重庆大学软件学院，重庆 401331

Abstract

Abstract: Web service recommendation based on Quality-of-Service （QoS） is of vital importance for users to find the proper Web service among huge numbers of functionally similar Web services. But current QoS predicting algorithms still have the data sparse problem and cannot predict the QoS values accurately. Focusing on these problems, a method called ClustTD is proposed in this paper. This method is based on location clustering and hierarchical tensor decomposition. Firstly, it clusters users and services into several local groups based on their location, and models local and global triadic tensors respectively for the relations of user -service-time. Then the hierarchical tensor decomposition is performed on the local and global triadic tensors. Finally, the predicted QoS value by local and global tensor decomposition is combined as the missing QoS values. Comprehensive experiment shows that this method achieves higher prediction accuracy and recommending quality of Web service, and can partly solve the problem of data sparse.

Key words: Web services recommendation, Quality-of-Service（QoS） properties, clustering, tensor decomposition

摘要： 基于服务质量（QoS）的Web服务推荐能在众多功能相似的Web服务中发现最能满足用户非功能需求的Web服务，但QoS属性值预测算法仍存在预测准确度不高和数据稀疏性的问题。针对以上问题，提出了一种基于位置聚类和分层张量分解的QoS预测算法ClustTD，该算法基于用户和服务的位置属性将用户和服务聚类成多个局部组，分别对局部组和全局的用户、服务和时间上下文进行张量建模和分解，将局部和全局张量分解的QoS预测值进行加权组合，同时考虑了局部和全局因素，获得最终QoS预测值。实验结果表明，该算法具有较高的QoS预测准确率和Web服务推荐质量，并能在一定程度上解决数据稀疏性问题。

关键词: Web服务推荐, 服务质量（QoS）属性, 聚类, 张量分解

TANG Ni1，2, XIONG Qingyu1，2, WANG Xibin1，2, GAO Min1，2, WEN Junhao1，2, ZENG Jun1，2. Web service recommendation based on location clustering and tensor decomposition[J]. Computer Engineering and Applications, 2016, 52(15): 65-72.

唐妮1，2，熊庆宇1，2，王喜宾1，2，高旻1，2，文俊浩1，2，曾骏1，2. 基于位置聚类和张量分解的Web服务推荐算法[J]. 计算机工程与应用, 2016, 52(15): 65-72.

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Web service recommendation based on location clustering and tensor decomposition

基于位置聚类和张量分解的Web服务推荐算法

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