CluCF: a clustering CF algorithm to address data sparsity problem

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren’t fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.     

From similarity perspective: a robust collaborative filtering approach for service recommendations

Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.     

基于监视的服务质量评估方法

在面向服务的体系结构(Service Oriented Architecture,SOA)中,消费者的目标是发现和使用高质量的服务。随着Web服务数量的不断增加,为用户推荐和选择最满足需求的Web服务已经成为服务计算领域最重要的挑战之一。在SOA中,传统的质量评估方法偏重于消费者获得更高的性能,未充分考虑消费者的个性化需求,这样,并不一定让消费者获得完全满足其需求的服务。因此,在服务选择和推荐过程中不仅需要考虑服务的功能性需求,还需要考虑服务的非功能性需求。而且,服务质量的改变只能在服务运行过程中才能被发现。为了解决上述问题,本文提出一种基于监视的服务质量评估方法,充分考虑服务的功能性和非功能性属性,建立统一模型,根据监视数据动态评估服务质量,为动态环境下选择和推荐服务提供依据。本文最后描述了该方法的实现框架。     

基于位置聚类和张量分解的Web服务推荐算法

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。     

基于可信推荐的服务质量评估模型

由于网络用户多样性和利益诉求的复杂性,部分用户发布的QoS数据不完全可信,以致影响了QoS评估的精度,为此提出基于可信推荐的QoS评估模型TR-SQE。该模型以用户推荐的与众不同程度作为其推荐信任度,隔离推荐信任度低于阈值的用户发布的QoS数据;TR-SQE将修正过的QOS信息作为推荐数据,接着根据用户与推荐者的偏好相似性来评估服务质量。分析和仿真结果表明,TR-SQE的平均绝对偏差MAE较其他方法小,评估结果与真实的服务质量基本相符,TR-SQE有助于用户的服务选择。     

QoS prediction for web services based on user-trust propagation model

There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user’s mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.     

Adaptive Service Composition in Flexible Processes

In advanced service oriented systems, complex applications, described as abstract business processes, can be executed by invoking a number of available Web services. End users can specify different preferences and constraints and service selection can be performed dynamically identifying the best set of services available at runtime. In this paper, we introduce a new modeling approach to the Web service selection problem that is particularly effective for large processes and when QoS constraints are severe. In the model, the Web service selection problem is formalized as a mixed integer linear programming problem, loops peeling is adopted in the optimization, and constraints posed by stateful Web services are considered. Moreover, negotiation techniques are exploited to identify a feasible solution of the problem, if one does not exist. Experimental results compare our method with other solutions proposed in the literature and demonstrate the effectiveness of our approach toward the identification of an optimal solution to the QoS constrained Web service selection problem     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

基于质量的数据挖掘服务选择

在面向服务的数据挖掘系统中各种数据挖掘的算法封装成 Web服务.用户选择合适的数据挖掘服务执行自己的数据挖掘任务,而大多数最终用户并不具备这样的专业知识.从方便用户的角度出发,系统需提供一套服务选择机制,来帮助用户选择高质量的数据挖掘服务.综合通用Web服务的评价标准、数据挖掘领域的专用评价因子及用户评价反馈等多种因素及服务的动态性,给出了一个较全面的数据挖掘服务评价本体,讨论了服务质量的评价方法,给出了基于服务质量评价的动态数据挖掘服务选择方法,用户可根据数据挖掘服务评价本体的语义模型,输入质量约束条件,也可以调整评价因子权值,系统在满足用户约束条件的服务集中,通过计算出服务的综合质量值,挑选最适合的算法执行.     

基于灰色关联分析的Web服务选择

为方便用户选择最优Web服务,利用灰色系统理论对Web服务质量QoS属性因子进行分析,提出了一种基于用户QoS偏好的Web服务灰色关联分析方法。考虑到Web服务QoS的不确定性,该方法使用区间对Web服务QoS值进行建模。为了确定候选服务的QoS与用户QoS需求的符合程度,先针对服务的每个QoS属性,计算其与用户QoS需求的灰色区间关联系数;然后结合各个QoS属性的关联系数计算候选服务的QoS与用户QoS需求的综合灰色区间关联度,关联度越大的服务越符合用户的要求;最后从满足用户功能需求的Web服务中选择灰色关联度最大的Web服务推荐给用户。与其它Web服务评价模型相比较,该模型更加符合Web服务QoS的实际情况,能够在服务QoS信息不充分、不确定的环境下,对QoS属性进行合乎实际的分析处理,从而得到更加合理有效的QoS评价。     

基于SOM神经网络的服务质量预测

服务质量预测在服务计算领域中是一个热点研究问题.在历史QoS数据稀疏的情况下,设计一个满足用户个性化需求的服务质量预测方法成为一项挑战.为解决这一挑战问题,本文提出一种基于SOM神经网络的服务质量预测方法SOMQP.首先,基于历史QoS数据,应用SOM神经网络算法分别对用户和服务进行聚类,得到用户关系矩阵和服务关系矩阵;进而,综合考虑用户信誉和服务关联性,采用一种新的Top-k选择机制获得相似用户和相似服务;最后,采用基于用户的和基于项目的混合策略对缺失QoS值进行预测.在真实的数据集WS-Dream上进行大量实验,结果表明,与经典的CF算法和K-means算法相比,本文方法较大程度上提高了QoS预测精度.     

Multi-user web service selection based on multi-QoS prediction

In order to find best services to meet multi-user’s QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.     

Research on dynamic business composition based on web service proxies

Web services-based business composition brings a number of advantages to the enterprise application development. How to select and compose the web services based on their functionality and QoS (Quality of Service) dynamically prove to be more and more important. In this paper we develop a proxy-based framework to compose Web services dynamically. The framework is featured with a QoS model, an effective service discovery and selection algorithms to facilitate the dynamic integration of Web services and management of abnormalities. Furthermore, a business process constructing method based on service slice is put forward to satisfy the users’ personalized requirements more effectively and flexibly. Our study concerns both functionality and QoS characteristics of Web services to identify the optimal business process solutions. A Complete case study is also included in this paper and the performance demonstrated that the framework and algorithms can provide a tangible and reliable solution to dynamic Web service composition and adaptation.     

基于特征项的群组信息推荐算法

个性化推荐系统采用知识发现技术给用户提供准确、合理的信息从而赢得客户。基于用户群组特征的推荐方式是当前在研究和实用两方面都取得一定成功的一种模式,但是这种算法的复杂度随着用户数量的增加而急剧增长,因此在实际的应用中,面对着数以万计的用户,服务系统要承担大负荷的计算量,从而导致推荐效率的下降。该文提出了一种基于特征项的推荐算法来解决基于用户的推荐算法所面临的可扩展性差的问题。实验表明,使用基于特征项的推荐算法能够在提高推荐效率的同时,达到或者超越基于用户的推荐算法的推荐性能。     

显式和隐式质量属性分离的可信服务质量评估模型

针对目前服务质量(QoS)评估方法中忽视对服务隐式质量的评估而导致结果不准确的问题,提出了一种综合考虑显式和隐式质量属性的服务评价方法。首先,显式质量属性以向量形式表达,采用服务质量评估模型,经过量化、归一化,计算出评估值;然后,隐式质量属性以用户评价间接表达,根据评价相似用户的推荐而完成对隐式服务质量的评价,评估过程考虑推荐用户的可信性和新老用户的区别;最后综合显式和隐式质量评价作为服务质量评价结果。使用100万条Web服务的QoS数据与其他3类算法进行了对比实验。仿真实验证明了所提方法的可行性与准确性。     

自动化服务推荐模型的研究及实现

在建立以Web Services技术为基础的大型平台时，服务的发现与集成是人们所面临的关键问题。在探索现有的服务发现机制的基础上，引入了智能化与QoS思想，对服务的发现进行研究与扩展，提出了一种自动化的服务推荐模型。它利用语义Web和Agent相结合的技术，在UDDI的基础上，自动搜索并执行与用户需求相匹配的服务或服务流程，并将执行的结果反馈给用户，供其作出选择。同时为了提高结果的可靠性，该模型将根据QoS需求，对执行中的服务或服务流进行筛选，并对执行后的结果进行排序，以提高服务推荐的质量。     

一种多参数的Web服务选择方法

随着服务计算的快速发展，如何快速而准确地找到最优的Web服务组合是众多挑战中最重要的一项。提出了一种基于二阶隐马尔可夫模型（HMM）的服务选择方法。该方法使用服务质量（QoS）参数去区分具有相同功能的Web服务，并且选择一组最优的Web服务来执行用户请求。通过考虑两个QoS参数-吞吐量和响应时间，该方法能够解决根据设定的阈值来衡量Web服务质量的问题。通过构建的模型和算法，方法能够选择出最优的Web服务以满足用户的需求。仿真实验验证了所提出的方法是有效的。     

Social network-based service recommendation with trust enhancement

Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.     

求解Web服务组合QoS优化的多属性决策及自适应遗传算法

随着面向服务计算(Service-oriented Computing,SOC)的不断发展,基于服务质量(Quality of Service,QoS)的Web服务组合研究成为了必然趋势。鉴于QoS属性的多维性及相互矛盾性,提出将基于QoS的Web服务组合优化问题转化为多属性决策问题进行求解。采用折中系数 对每个组合服务实例到正负理想点的距离进行累加求和,最终得到一组最优服务排序结果,用户可以根据自身偏好进行选择。传统的多属性决策方法无法有效地处理大规模的组合服务搜索空间,因此,为了有效地解决Web服务组合优化这一NP难题,提出一种结合多属性决策方法和自适应遗传算法的新型优化算法来解决该问题。实验采用真实的QoS综合服务数据集进行验证,实验结果表明,该方法能够在较短时间内找到全局近似最优解,且解集的排序结果接近于实际的最优服务排序。同时,该方法对于解决大规模的Web服务组合优化问题具有良好的可伸缩性。     

基于动态QoS的Web服务组合

在Web服务组合中,现行的几种QoS衡量标准都将重点放在单个Web服务本身的质量上,而忽视了Web服务动态特性、组合特性以及服务组合中的网络特性。另外,在诸多服务组合的算法中,都只是强调组合服务的总体质量,却忽略了用户对某些质量属性的约束条件,从而导致服务重计算问题经常发生。为此,考虑了服务动态特性以及服务间的协作关系对组合服务质量的影响,提出了动态QoS模型;同时,综合了用户的质量约束以及组合服务的整体质量,将用户的质量约束引入服务组合流程中。最后通过实验证实了所提出的动态QoS模型能够根据服务实体的实时情况计算服务质量,同时将用户的质量约束引入服务组合流程中,有效地避免了服务重计算问题。