基于协同过滤与QoS的个性化Web服务推荐研究
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摘要
随着互联网技术的不断发展，Web服务推荐与选择已经逐渐成为学术界和工业界共同关注的重要研究内容。随着Web服务数量的不断增加，为用户推荐和选择最优的Web服务已经成为服务计算领域内最重要的挑战之一。在一组具有多个相同或者相似功能性属性的Web候选服务中，为了帮助用户选择满足他们需求的最优的Web服务需要同时考虑服务质量的功能性和非功能性属性。此外，Web服务的QoS值在运行时刻可能会因为服务器超载，网络条件等多种因素的影响而发生变化。因此以往使用静态的QoS评估方法已不再适用于Web服务的动态环境，这需要一种机制能够感知动态环境下Web服务质量的变化情况。本论文使用基于协同过滤与QoS的个性化Web服务推荐算法以解决上述问题。针对当前Web服务推荐算法不足的现状，提出将服务相异性和情境因素引入到Web服务推荐领域之中以提高推荐的性能；此外还提出了两种新的混合Web服务推荐算法以解决目前不同推荐算法之间的权重确定问题。现将论文的主要研究内容和成果概括如下：
①对Web服务的发展进行综述，分析了Web服务目前所面临的一些问题。总结归纳了现有Web服务技术的主要研究方向，及其各自的特点和所面临的挑战。并对Web服务的研究进展进行总结与归纳，引出本文研究内容的意义所在，为后续的研究提供相应的理论基础。
②对协同过滤技术进行简要介绍，简述了协同过滤技术的发展历史，及其重要的学术与商业意义。对现有主流的协同过滤技术进行分类，分析其特点和各自适用范围。为基于协同过滤与QoS的个性化Web服务推荐研究奠定理论基础。
③把协同过滤技术引入到Web服务推荐之中，构建基于QoS的个性化Web服务推荐以解决服务领域中的动态性、个性化和情境缺失问题。其基本思想是通过对用户-服务QoS属性值矩阵进行个性化分析，使用协同过滤技术预测缺省QoS值的Web服务质量，将获得的预测QoS值以一定的规则进行排序，从而把具有最优预测QoS值的Web服务推荐给用户。首先提出面向服务相异性的单一协同过滤技术的Web服务推荐算法，在使用真实QoS数据集Web服务推荐领域中首次引入Web服务的相异性特征，实验结果表明Web服务之间的相异性比相似性更能反映Web服务之间的关系，基于相异性的预测方法与传统的基于相似性的预测方法相比具有更高的预测准确性。其次，针对目前混合型Web服务推荐算法中各推荐算法权重难以确定的难题，首次提出基于神经网络的两种新的混合型Web服务推荐算法，它们分别采用BP和RBF神经网络对不同的协同过滤算法进行权重训练。该混合型算法结合了不同算法的优势，实验结果表明这两种混合型算法与目前最新水平的著名WSRec算法相比具有更优的预测准确性和时间效率。最后，提出了面向个性化情境的Web服务推荐算法，为解决情境缺失问题提供了一种新的解决方案。实验结果表明，用户和Web服务的情境因素对Web服务推荐的性能具有重要的影响，在推荐的过程中考虑情境因素不仅能保持推荐的准确性而且能显著提高推荐的时间性能。
④使用真实环境下大规模的QoS数据集实施大量的实验以验证推荐方法的性能。该数据集包含150万条Web服务调用记录，这些服务是从网络上获得的21，197个开放Web服务中任意地选出100Web服务提供给来自20多个国家的Web服务用户调用。该数据集是目前已公布的真实环境下Web服务数据集中规模最大的数据集，
⑤本文以国家自然科学基金“大型分布式软件系统的行为监控与可信演化”为背景。从基于协同过滤与QoS的个性化Web服务推荐方法出发，通过对Web服务的QoS值进行预测评估，研究分布式系统下Web服务质量的变化情况，从而驱动分布式系统的演化。构建了服务质量驱动的Web服务演化系统，并提出系统的体系结构、功能模块设计。
The rising development of internet technology, web service selection andrecommendation is becoming an important research problem attracting great attentionsfrom both academia and industry researchers. With the number increasing of Webservices, recommending and selecting optimal Web services for users has become oneof the most challenging issues in the field of service computing. In the presence ofmultiple Web services with conform or similar functionalities, both functional andnonfunctional Quality-of-Service (QoS) attributes should be taken into account to helpuser selecting the most suitable services according to their needs. QoS of Web servicescan change at run time due to various reasons (e.g., server workload, network condition,etc.). Consequently, methods where Web services are statically evaluated areinappropriate. Instead, a new approach is needed, in which runtime changes in the QoSof the services are taken into account. To address these problems, this thesis proposesseveral methods based on Collaborative Filtering (CF) and QoS for personalized webservice recommendation. To overcome the drawbacks described above, differencebetween services and context are introduced in Web service recommedation for betterrecommendation accuracy. Furthermore, two novel hybrid collaborative filteringalgorithms are proposed to solve the problem of weights between different collaborativefiltering methods. The main research and contributions of this thesis is described asfollowing:
①An overview of current development and problems of Web services is given.And the main research areas, special characteristics and critical challenges of Webservices are also summarized. Then research progress on Web services technology issummarized and classified, which introduces the significance of research in this thesisand provides the correlative theory for further research.
②A brief introduction to collaborative filtering technology is presented. And thedevelopment of collaborative filtering is also analyzed, which includes the importantacademic and commercial significance. Furthermore, the main technology ofcollaborative filtering is summarized, in which the characteristics and applicationscenarios is also analyzed. These analyses have set up the theoretical foundation forresearch on personalized web services recommendation by QoS and collaborative filtering.
③The collaborative filtering is introduced in web services recommendation areato construct a personalized web services recommendation system based on CF and QoSfor addressing the problems of dynamic, personalization and context-free. According touser-service matrix of QoS values, the missing QoS values of web services can beobtained by CF method. The predicted QoS values will be ranked according to certainrules, and then the web services candidate with optimal predicted QoS performance canbe recommended to users. Firstly, a web services recommendation method viadifference of services is proposed, and the difference of services is first introduced inpersonalized web services recommendation using real-world QoS dataset, theexperimental results demonstrate that the difference between services is moreappropriate to present the relationship between services than similarity and thedifference based method provides better prediction accuracy than other similarity basedmethod. Secondly, to address the problem of how to identify the weights betweendifferent CF methods in hybrid web service recommendation, two novel hybridrecommendation methods of web services based on neural network are first proposed, inwhich BP and RBF neural networks are employed to train weights among different CFmethods, respectively. These hybrid methods combine the advantages of different CFmethods, and experiments show that our hybrid methods achieve better predictionaccuracy and time complexity than the state-of-the art method WSRec. Finally, toovercome the context-free problem in web services recommendation, a personalizedcontext-aware recommendation method for web services is proposed. The experimentspresent that the context of users and web services have great influence on performanceof web services recommendation, and the context-aware method can provide bothefficient and effective recommendation.
④Comprehensive experiments are conducted employing real-world Web serviceQoS dataset to present the recommendation performance of proposed methods in thisthesis. The large-scale real-world dataset includes1.5millions Web service invocations,and these web services are randomly selected100Web services from totally21,197publicly available Web services to be invoked by service users in more than20countries. This dataset is the largest one among the published work of the real-worldQoS data set.
⑤This thesis is under the background of national natural science foundation "The behavior monitoring and dependable evolution of large distributed software system ".According to the predicted QoS values provided by our personalized web servicerecommendation methods based on CF and QoS, research on the changes of QoS forweb services in distributed systems, then the evolution of distributed systems can bedriven. The system of QoS driven web services evolution is constructed, and thearchitecture, functional modules are also provided.
①对Web服务的发展进行综述，分析了Web服务目前所面临的一些问题。总结归纳了现有Web服务技术的主要研究方向，及其各自的特点和所面临的挑战。并对Web服务的研究进展进行总结与归纳，引出本文研究内容的意义所在，为后续的研究提供相应的理论基础。
②对协同过滤技术进行简要介绍，简述了协同过滤技术的发展历史，及其重要的学术与商业意义。对现有主流的协同过滤技术进行分类，分析其特点和各自适用范围。为基于协同过滤与QoS的个性化Web服务推荐研究奠定理论基础。
③把协同过滤技术引入到Web服务推荐之中，构建基于QoS的个性化Web服务推荐以解决服务领域中的动态性、个性化和情境缺失问题。其基本思想是通过对用户-服务QoS属性值矩阵进行个性化分析，使用协同过滤技术预测缺省QoS值的Web服务质量，将获得的预测QoS值以一定的规则进行排序，从而把具有最优预测QoS值的Web服务推荐给用户。首先提出面向服务相异性的单一协同过滤技术的Web服务推荐算法，在使用真实QoS数据集Web服务推荐领域中首次引入Web服务的相异性特征，实验结果表明Web服务之间的相异性比相似性更能反映Web服务之间的关系，基于相异性的预测方法与传统的基于相似性的预测方法相比具有更高的预测准确性。其次，针对目前混合型Web服务推荐算法中各推荐算法权重难以确定的难题，首次提出基于神经网络的两种新的混合型Web服务推荐算法，它们分别采用BP和RBF神经网络对不同的协同过滤算法进行权重训练。该混合型算法结合了不同算法的优势，实验结果表明这两种混合型算法与目前最新水平的著名WSRec算法相比具有更优的预测准确性和时间效率。最后，提出了面向个性化情境的Web服务推荐算法，为解决情境缺失问题提供了一种新的解决方案。实验结果表明，用户和Web服务的情境因素对Web服务推荐的性能具有重要的影响，在推荐的过程中考虑情境因素不仅能保持推荐的准确性而且能显著提高推荐的时间性能。
④使用真实环境下大规模的QoS数据集实施大量的实验以验证推荐方法的性能。该数据集包含150万条Web服务调用记录，这些服务是从网络上获得的21，197个开放Web服务中任意地选出100Web服务提供给来自20多个国家的Web服务用户调用。该数据集是目前已公布的真实环境下Web服务数据集中规模最大的数据集，
⑤本文以国家自然科学基金“大型分布式软件系统的行为监控与可信演化”为背景。从基于协同过滤与QoS的个性化Web服务推荐方法出发，通过对Web服务的QoS值进行预测评估，研究分布式系统下Web服务质量的变化情况，从而驱动分布式系统的演化。构建了服务质量驱动的Web服务演化系统，并提出系统的体系结构、功能模块设计。
The rising development of internet technology, web service selection andrecommendation is becoming an important research problem attracting great attentionsfrom both academia and industry researchers. With the number increasing of Webservices, recommending and selecting optimal Web services for users has become oneof the most challenging issues in the field of service computing. In the presence ofmultiple Web services with conform or similar functionalities, both functional andnonfunctional Quality-of-Service (QoS) attributes should be taken into account to helpuser selecting the most suitable services according to their needs. QoS of Web servicescan change at run time due to various reasons (e.g., server workload, network condition,etc.). Consequently, methods where Web services are statically evaluated areinappropriate. Instead, a new approach is needed, in which runtime changes in the QoSof the services are taken into account. To address these problems, this thesis proposesseveral methods based on Collaborative Filtering (CF) and QoS for personalized webservice recommendation. To overcome the drawbacks described above, differencebetween services and context are introduced in Web service recommedation for betterrecommendation accuracy. Furthermore, two novel hybrid collaborative filteringalgorithms are proposed to solve the problem of weights between different collaborativefiltering methods. The main research and contributions of this thesis is described asfollowing:
①An overview of current development and problems of Web services is given.And the main research areas, special characteristics and critical challenges of Webservices are also summarized. Then research progress on Web services technology issummarized and classified, which introduces the significance of research in this thesisand provides the correlative theory for further research.
②A brief introduction to collaborative filtering technology is presented. And thedevelopment of collaborative filtering is also analyzed, which includes the importantacademic and commercial significance. Furthermore, the main technology ofcollaborative filtering is summarized, in which the characteristics and applicationscenarios is also analyzed. These analyses have set up the theoretical foundation forresearch on personalized web services recommendation by QoS and collaborative filtering.
③The collaborative filtering is introduced in web services recommendation areato construct a personalized web services recommendation system based on CF and QoSfor addressing the problems of dynamic, personalization and context-free. According touser-service matrix of QoS values, the missing QoS values of web services can beobtained by CF method. The predicted QoS values will be ranked according to certainrules, and then the web services candidate with optimal predicted QoS performance canbe recommended to users. Firstly, a web services recommendation method viadifference of services is proposed, and the difference of services is first introduced inpersonalized web services recommendation using real-world QoS dataset, theexperimental results demonstrate that the difference between services is moreappropriate to present the relationship between services than similarity and thedifference based method provides better prediction accuracy than other similarity basedmethod. Secondly, to address the problem of how to identify the weights betweendifferent CF methods in hybrid web service recommendation, two novel hybridrecommendation methods of web services based on neural network are first proposed, inwhich BP and RBF neural networks are employed to train weights among different CFmethods, respectively. These hybrid methods combine the advantages of different CFmethods, and experiments show that our hybrid methods achieve better predictionaccuracy and time complexity than the state-of-the art method WSRec. Finally, toovercome the context-free problem in web services recommendation, a personalizedcontext-aware recommendation method for web services is proposed. The experimentspresent that the context of users and web services have great influence on performanceof web services recommendation, and the context-aware method can provide bothefficient and effective recommendation.
④Comprehensive experiments are conducted employing real-world Web serviceQoS dataset to present the recommendation performance of proposed methods in thisthesis. The large-scale real-world dataset includes1.5millions Web service invocations,and these web services are randomly selected100Web services from totally21,197publicly available Web services to be invoked by service users in more than20countries. This dataset is the largest one among the published work of the real-worldQoS data set.
⑤This thesis is under the background of national natural science foundation "The behavior monitoring and dependable evolution of large distributed software system ".According to the predicted QoS values provided by our personalized web servicerecommendation methods based on CF and QoS, research on the changes of QoS forweb services in distributed systems, then the evolution of distributed systems can bedriven. The system of QoS driven web services evolution is constructed, and thearchitecture, functional modules are also provided.
引文
①Z. B. Zheng, H. Ma, M. Lyu, and I. King. QoS-Aware Web Service Recommendation by Collaborative Filtering [J].IEEE Transactions on Service Computing (TSC),2011,4(2):140-152.
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