本文選題：Web服務　切入點：用戶信息　出處：《南京大學》2014年碩士論文

【摘要】：作為一種基于互聯(lián)網(wǎng)標準和XML技術的新型分布式計算模型,Web服務在電子商務和企業(yè)應用集成等分布式平臺上發(fā)揮越來越重要的作用。隨著互聯(lián)網(wǎng)上Web服務數(shù)量的指數(shù)型增長,如何主動感知用戶需求、挖掘用戶個人偏好并為用戶提供最感興趣的服務選擇列表,已經(jīng)成為Web服務研究領域的熱點問題。目前Web服務推薦研究中最為常用的是協(xié)同過濾算法,包括基于用戶的協(xié)同過濾、基于服務的協(xié)同過濾以及兩者的結合。協(xié)同過濾算法中最容易出現(xiàn)的就是稀疏矩陣和冷啟動問題,所以針對傳統(tǒng)的協(xié)同過濾算法中存在的不足,本文將社交網(wǎng)絡結合到Web服務推薦算法中,提出了一種基于用戶信任網(wǎng)絡和偏好的Web服務推薦算法,即首先提出基于用戶關系和偏好的服務推薦算法,在驗證算法有效性的基礎上,通過深入挖掘用戶信息構建信任網(wǎng)絡,將用戶偏好算法和信任網(wǎng)絡相結合,為用戶提供更為有效的服務推薦。論文的主要貢獻如下：首先,針對Web服務QoS屬性的多樣性,提出了一種基于多QoS值的相似度計算方法,這種方法可以直接計算有多種QoS屬性的服務的相似性和訪問服務的用戶的相似性。在此基礎上,提出了一種基于用戶關系和偏好的Web服務推薦算法,通過使用服務信息對服務進行聚類,將用戶-服務矩陣轉化為用戶-服務類矩陣來實現(xiàn)稀疏矩陣降維,并從社交網(wǎng)絡中獲取充分的用戶信息和用戶關系。通過挖掘用戶和服務類之間的關系,根據(jù)用戶偏好將用戶劃分為不同的興趣類并提取出每個類的顯著用戶特征,再結合社交網(wǎng)絡中的新用戶信息和興趣標簽,通過與用戶興趣類的用戶特征和服務類標簽進行比對,完成對新用戶的推薦,從而解決推薦系統(tǒng)的冷啟動問題。其次,提出了一種根據(jù)用戶信息構建用戶信任網(wǎng)絡模型的方法,可充分利用社交網(wǎng)絡中的用戶信息,深入挖掘用戶潛在關系。同時,為了提高推薦的準確性,使用主成分分析算法對用戶偏好算法進行優(yōu)化,對同一興趣類中的用戶關系進行更為嚴格的劃分,并將用戶信任網(wǎng)絡與之結合,構成了基于用戶信任網(wǎng)絡和偏好的Web服務推薦算法。與用戶關系偏好算法相比,這種推薦算法在擴充服務推薦范圍的基礎上又提高了服務篩選的標準,既考慮了用戶偏好的相似性,又深入挖掘了用戶的潛在信任關系,可以為新用戶推薦滿足用戶需求且具有一定可信度的服務。再次,在工具實現(xiàn)和實驗分析上,完成了Web服務推薦工具的開發(fā),并針對基于用戶關系和偏好、基于用戶信任網(wǎng)絡和偏好的服務推薦算法進行了充分的實驗。實驗結果顯示,與傳統(tǒng)的基于協(xié)同過濾算法相比,文中提出的兩種推薦算法具有更高的推薦準確率,尤其是基于用戶信任網(wǎng)絡和偏好的推薦算法,其推薦準確率在基于用戶關系和偏好的推薦算法基礎上有明顯提高。
[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.
【學位授予單位】：南京大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.3;TP393.09

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