本文選題：Web服務(wù) + 協(xié)同過濾　； 參考：《山東大學(xué)》2014年碩士論文

【摘要】：近年來,互聯(lián)網(wǎng)技術(shù)發(fā)展日新月異,Web服務(wù)越來越得到重視。為用戶選擇和推薦最優(yōu)的Web服務(wù),一直是服務(wù)計(jì)算領(lǐng)域的核心問題。隨著Web服務(wù)的種類與數(shù)量的增多,服務(wù)推薦的難度也會(huì)隨之增加。在大量的功能相同或相似的Web服務(wù)中,根據(jù)用戶特征與需求,考慮服務(wù)的功能性與非功能性屬性,為用戶推薦Web服務(wù)是本文主要研究的內(nèi)容。 目前主要的推薦技術(shù)包括基于內(nèi)容的推薦,基于關(guān)聯(lián)規(guī)則的推薦,協(xié)同過濾推薦等。協(xié)同過濾推薦方法是推薦系統(tǒng)中重要的方法之一,最早出現(xiàn)在B2C的電子商務(wù)領(lǐng)域,具有良好的應(yīng)用和發(fā)展前景。商家可以根據(jù)用戶的偏好與興趣為用戶推薦,推薦其可能喜歡或選擇的產(chǎn)品,比如音樂、圖書、影像作品等。盡管協(xié)同過濾推薦方法在Web服務(wù)領(lǐng)域有一定優(yōu)勢,但仍然存在許多問題。新用戶問題、新對象問題、稀疏矩陣問題等一直是協(xié)同過濾方法研究的熱點(diǎn)。隨著Web服務(wù)數(shù)量與用戶的增多,問題帶來的弊端也更加明顯,如何有效的解決這些問題,提高推薦系統(tǒng)的性能是本文研究的重點(diǎn)。 在本文中,首先,針對協(xié)同過濾技術(shù)與Web服務(wù)QoS信息特點(diǎn)相結(jié)合,提出了根據(jù)用戶對Web服務(wù)QoS信息偏好建立基于用戶特征的用戶相似度模型。原有的用戶相似度計(jì)算模型僅考慮用戶歷史評分,考慮用戶歷史評分并不能完全表現(xiàn)用戶的偏好。比如兩個(gè)用戶共同選擇同一個(gè)Web服務(wù),其中一個(gè)用戶可能更關(guān)心Web服務(wù)的響應(yīng)時(shí)間,而另一個(gè)用戶看重的是Web服務(wù)的安全性。該模型在用戶歷史評分信息的基礎(chǔ)上,深入挖掘用戶選擇與Web服務(wù)QoS信息的關(guān)系,為用戶進(jìn)行細(xì)分,最終基于用戶特征計(jì)算用戶之問的相似度,并通過實(shí)驗(yàn)驗(yàn)證了該模型的實(shí)用性。然后,對于協(xié)同過濾方法存在的新用戶問題以及新對象問題,本文提出建立用戶專業(yè)度模型。通過用戶在某領(lǐng)域的涉及度,以及用戶評分的準(zhǔn)確度,衡量用戶的專業(yè)度。在為新用戶推薦時(shí),由于缺少新用戶的評分信息,采取結(jié)合相似用戶與專業(yè)用戶的評分信息為其推薦。專業(yè)用戶可以較準(zhǔn)確的評價(jià)新對象,新對象問題也得到一定解決,本文通過實(shí)驗(yàn)進(jìn)行了研究與總結(jié)。最后,針對協(xié)同過濾方法中的稀疏矩陣問題,分析了稀疏矩陣問題會(huì)引起的相似用戶數(shù)量不足,本文提出了用戶相似度傳遞模型。根據(jù)相似用戶之間的評分項(xiàng)集,建立了用戶信任度模型,在用戶信任度模型的基礎(chǔ)上,為用戶傳遞相似性,提高相似用戶的數(shù)量,本文運(yùn)用實(shí)驗(yàn)分析與研究了模型的可行性。
[Abstract]:In recent years, with the rapid development of Internet technology, more and more attention has been paid to Web services. Choosing and recommending the best Web service for users is always the core problem in service computing field. With the increase of Web service types and quantity, the difficulty of service recommendation will increase. In a large number of Web services with the same or similar functions, considering the functional and non-functional attributes of the services according to the characteristics and requirements of users, recommending Web services for users is the main content of this paper. At present, the main recommendation technologies include content based recommendation, association rule based recommendation, collaborative filtering recommendation and so on. Collaborative filtering recommendation method is one of the most important methods in recommendation system. It first appeared in the field of electronic commerce of B2C, and has a good application and development prospect. Merchants can recommend products they may like or choose according to their preferences and interests, such as music, books, video works and so on. Although collaborative filtering recommendation method has some advantages in the field of Web services, there are still many problems. New user problem, new object problem and sparse matrix problem have been the focus of collaborative filtering. With the increase of the number of Web services and users, the disadvantages brought by the problems are more obvious. How to effectively solve these problems and improve the performance of recommendation system is the focus of this paper. In this paper, firstly, a user similarity model based on users' preference for Web services QoS information is proposed according to the combination of collaborative filtering technology and QoS information characteristics of Web services. The original user similarity calculation model only considers the user history score, and the user history score can not completely express the user preference. For example, when two users choose the same Web service together, one user may be more concerned about the response time of the Web service, while the other user is concerned about the security of the Web service. On the basis of user history scoring information, this model deeply excavates the relationship between user selection and Web service QoS information, subdivides users, and calculates the similarity of user questions based on user characteristics. The practicability of the model is verified by experiments. Then, for the new user problem and the new object problem of collaborative filtering method, this paper proposes a user professional model. The user's degree of professionalism is measured by the user's involvement in a field and the accuracy of the user's score. When recommending for new users, because of the lack of rating information of new users, it is recommended by combining the rating information of similar users with that of professional users. Professional users can evaluate new objects more accurately, and the problem of new objects can be solved to some extent. Finally, aiming at the sparse matrix problem in collaborative filtering, the shortage of similar users caused by sparse matrix problem is analyzed, and a user similarity transfer model is proposed in this paper. According to the score item set of similar users, a user trust model is established. On the basis of user trust model, the similarity is transmitted to users and the number of similar users is increased. The feasibility of the model is analyzed and studied by experiments in this paper.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09;TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 張忠林;曹志宇;李元韜;;基于加權(quán)歐式距離的k_means算法研究[J];鄭州大學(xué)學(xué)報(bào)(工學(xué)版);2010年01期

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本文編號：1829690