基于用户信任网络和偏好的Web服务推荐

发布时间：2018-04-10 01:06

本文选题：Web服务　切入点：用户信息　出处：《南京大学》2014年硕士论文

【摘要】：作为一种基于互联网标准和XML技术的新型分布式计算模型,Web服务在电子商务和企业应用集成等分布式平台上发挥越来越重要的作用。随着互联网上Web服务数量的指数型增长,如何主动感知用户需求、挖掘用户个人偏好并为用户提供最感兴趣的服务选择列表,已经成为Web服务研究领域的热点问题。目前Web服务推荐研究中最为常用的是协同过滤算法,包括基于用户的协同过滤、基于服务的协同过滤以及两者的结合。协同过滤算法中最容易出现的就是稀疏矩阵和冷启动问题,所以针对传统的协同过滤算法中存在的不足,本文将社交网络结合到Web服务推荐算法中,提出了一种基于用户信任网络和偏好的Web服务推荐算法,即首先提出基于用户关系和偏好的服务推荐算法,在验证算法有效性的基础上,通过深入挖掘用户信息构建信任网络,将用户偏好算法和信任网络相结合,为用户提供更为有效的服务推荐。论文的主要贡献如下：首先,针对Web服务QoS属性的多样性,提出了一种基于多QoS值的相似度计算方法,这种方法可以直接计算有多种QoS属性的服务的相似性和访问服务的用户的相似性。在此基础上,提出了一种基于用户关系和偏好的Web服务推荐算法,通过使用服务信息对服务进行聚类,将用户-服务矩阵转化为用户-服务类矩阵来实现稀疏矩阵降维,并从社交网络中获取充分的用户信息和用户关系。通过挖掘用户和服务类之间的关系,根据用户偏好将用户划分为不同的兴趣类并提取出每个类的显著用户特征,再结合社交网络中的新用户信息和兴趣标签,通过与用户兴趣类的用户特征和服务类标签进行比对,完成对新用户的推荐,从而解决推荐系统的冷启动问题。其次,提出了一种根据用户信息构建用户信任网络模型的方法,可充分利用社交网络中的用户信息,深入挖掘用户潜在关系。同时,为了提高推荐的准确性,使用主成分分析算法对用户偏好算法进行优化,对同一兴趣类中的用户关系进行更为严格的划分,并将用户信任网络与之结合,构成了基于用户信任网络和偏好的Web服务推荐算法。与用户关系偏好算法相比,这种推荐算法在扩充服务推荐范围的基础上又提高了服务筛选的标准,既考虑了用户偏好的相似性,又深入挖掘了用户的潜在信任关系,可以为新用户推荐满足用户需求且具有一定可信度的服务。再次,在工具实现和实验分析上,完成了Web服务推荐工具的开发,并针对基于用户关系和偏好、基于用户信任网络和偏好的服务推荐算法进行了充分的实验。实验结果显示,与传统的基于协同过滤算法相比,文中提出的两种推荐算法具有更高的推荐准确率,尤其是基于用户信任网络和偏好的推荐算法,其推荐准确率在基于用户关系和偏好的推荐算法基础上有明显提高。
[Abstract]:As a new distributed computing model based on Internet standards and XML technology, web services play an increasingly important role in distributed platforms such as e-commerce and enterprise application integration.With the exponential growth of the number of Web services on the Internet, it has become a hot issue in the research field of Web services that how to actively perceive user needs, mine user preferences and provide users with the most interesting list of service choices.At present, collaborative filtering algorithms are the most commonly used in the research of Web services recommendation, including user-based collaborative filtering, service-based collaborative filtering and the combination of the two.The problem of sparse matrix and cold start is the most common problem in the collaborative filtering algorithm. Therefore, in view of the shortcomings of the traditional collaborative filtering algorithm, this paper combines the social network into the Web services recommendation algorithm.In this paper, a Web service recommendation algorithm based on user trust network and preference is proposed. Firstly, a service recommendation algorithm based on user relationship and preference is proposed. On the basis of verifying the validity of the algorithm, the trust network is constructed by mining user information deeply.The user preference algorithm and trust network are combined to provide more efficient service recommendation for users.The main contributions of this paper are as follows: firstly, a similarity calculation method based on multiple QoS values is proposed for the diversity of QoS attributes of Web services.This method can directly calculate the similarity of services with multiple QoS attributes and the similarity of users accessing services.On this basis, a Web service recommendation algorithm based on user relationship and preference is proposed. By using service information to cluster services, the user-service matrix is transformed into user-service class matrix to realize sparse matrix dimensionality reduction.And from the social network to obtain adequate user information and user relations.By mining the relationship between users and service classes, the users are divided into different interest classes according to their preferences, and the salient user characteristics of each class are extracted, and then the new user information and interest tags in social networks are combined.By comparing with the user characteristics of user interest class and the label of service class, the recommendation of new users is completed, and the cold start problem of recommendation system is solved.Secondly, a method of constructing user trust network model based on user information is proposed, which can make full use of user information in social network and tap the potential relationship of users.At the same time, in order to improve the accuracy of recommendation, the principal component analysis (PCA) algorithm is used to optimize the user preference algorithm, the user relationship in the same interest class is more strictly divided, and the user trust network is combined with it.A Web service recommendation algorithm based on user trust network and preference is constructed.Compared with the user relationship preference algorithm, this recommendation algorithm not only improves the standard of service selection on the basis of extending the range of service recommendation, but also takes into account the similarity of user preference and excavates the potential trust relationship of users.New users can recommend services that meet their needs and have a certain degree of credibility.Thirdly, in the aspect of tool implementation and experimental analysis, the development of Web service recommendation tool is completed, and a full experiment is carried out on the service recommendation algorithm based on user relationship and preference, based on user trust network and preference.The experimental results show that compared with the traditional collaborative filtering algorithm, the proposed two recommendation algorithms have higher recommendation accuracy, especially the recommendation algorithm based on user trust network and preference.The recommendation accuracy is improved obviously on the basis of the recommendation algorithm based on user relationship and preference.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.3;TP393.09

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