基于QoS及協(xié)同過濾的Web服務推薦方法研究
發(fā)布時間：2018-11-03 11:20
【摘要】：隨著互聯(lián)網(wǎng)技術的不斷發(fā)展，Web服務推薦與選擇已經(jīng)逐漸成為學術界和工業(yè)界共同關注的重要研究內(nèi)容,服務質(zhì)量(QoS)是成功進行Web服務推薦的關鍵性因素。然而，，Web服務的QoS值在運行時刻可能會因為服務器超載，網(wǎng)絡條件等多種因素的影響而發(fā)生變化。由于Web服務環(huán)境的動態(tài)性，目前現(xiàn)有的服務選擇方法通常無法有效地涵蓋QoS內(nèi)在的不確定性，使得服務推薦結果與實際結果偏差較大。為解決Web服務的QoS值的動態(tài)性以及目前算法忽視QoS內(nèi)在的不確定性,導致服務選擇可靠性差問題，本文提出一種改進的基于協(xié)同過濾的Web服務推薦方法，該方法的引入使得服務用戶不需要對Web服務進行調(diào)用，只需要對歷史的Web服務的QoS信息進行分析挖掘就能找出適合用戶的最優(yōu)Web服務。 本文提出的推薦算法不同于傳統(tǒng)的推薦算法，主要表現(xiàn)在以下幾個方面：在服務可靠性方面，本文引入云模型中的逆向云算法來解決QoS內(nèi)在不確定性導致的服務選擇可靠性差問題，把不可靠的服務剔除；在相似度計算方面，本文算法在計算用戶間相似度時，充分考慮了Web服務的內(nèi)在特征，在計算服務間相似度時，充分考慮了用戶的內(nèi)在特征；在對QoS缺省值預測方面，為了緩解負數(shù)對預測性能的影響，本文對傳統(tǒng)的基于服務的QoS預測算法和基于用戶的QoS預測算法進行改進；當為目標用戶預測的QoS值為負數(shù)時，使用服務或者用戶QoS值算術平均的方法進行計算填充。最后聯(lián)合基于服務的QoS預測算法和基于用戶的QoS預測算法采用自適應均衡權重的方法給出最終的QoS預測結果。為驗證本文提出算法的優(yōu)越性，本文使用了真實環(huán)境下大規(guī)模的QoS數(shù)據(jù)集進行仿真實驗，該數(shù)據(jù)集包含了1500000條Web服務調(diào)用記錄，通過仿真對比實驗證明了本文算法的優(yōu)越性。
[Abstract]:With the continuous development of Internet technology, Web service recommendation and selection has gradually become an important research content of academia and industry. Quality of service (QoS) is the key factor for successful Web service recommendation. However, the QoS value of Web services may change at runtime due to the influence of server overload, network conditions and other factors. Because of the dynamic nature of the Web service environment, the existing service selection methods usually can not effectively cover the inherent uncertainty of QoS, which makes the service recommendation results deviate greatly from the actual results. In order to solve the dynamic QoS value of Web services and ignore the inherent uncertainty of QoS in current algorithms, this paper proposes an improved Web service recommendation method based on collaborative filtering, which results in poor reliability of service selection. With the introduction of this method, service users do not need to invoke Web services, but only need to analyze and mine the QoS information of historical Web services to find out the best Web services suitable for users. The recommendation algorithm proposed in this paper is different from the traditional recommendation algorithm, mainly in the following aspects: in terms of service reliability, In this paper, the reverse cloud algorithm in cloud model is introduced to solve the problem of poor reliability of service selection caused by the inherent uncertainty of QoS, and the unreliable services are eliminated. In the aspect of similarity calculation, when computing the similarity between users, the algorithm takes into account the inherent features of Web services, and the inherent characteristics of users when computing the similarity between services. In the aspect of QoS default prediction, in order to mitigate the influence of negative number on prediction performance, this paper improves the traditional QoS prediction algorithm based on service and the QoS prediction algorithm based on user. When the predicted QoS value for the target user is negative, the service or the user QoS arithmetic average method is used to calculate the population. Finally, the QoS prediction algorithm based on services and the QoS prediction algorithm based on users are combined to give the final QoS prediction results using the adaptive equalization weight method. In order to verify the superiority of the proposed algorithm, this paper uses a large scale QoS data set in real environment to carry out simulation experiments. The dataset contains 1500000 records of Web service calls, and the superiority of the proposed algorithm is proved by simulation and comparison experiments.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
[Abstract]:With the continuous development of Internet technology, Web service recommendation and selection has gradually become an important research content of academia and industry. Quality of service (QoS) is the key factor for successful Web service recommendation. However, the QoS value of Web services may change at runtime due to the influence of server overload, network conditions and other factors. Because of the dynamic nature of the Web service environment, the existing service selection methods usually can not effectively cover the inherent uncertainty of QoS, which makes the service recommendation results deviate greatly from the actual results. In order to solve the dynamic QoS value of Web services and ignore the inherent uncertainty of QoS in current algorithms, this paper proposes an improved Web service recommendation method based on collaborative filtering, which results in poor reliability of service selection. With the introduction of this method, service users do not need to invoke Web services, but only need to analyze and mine the QoS information of historical Web services to find out the best Web services suitable for users. The recommendation algorithm proposed in this paper is different from the traditional recommendation algorithm, mainly in the following aspects: in terms of service reliability, In this paper, the reverse cloud algorithm in cloud model is introduced to solve the problem of poor reliability of service selection caused by the inherent uncertainty of QoS, and the unreliable services are eliminated. In the aspect of similarity calculation, when computing the similarity between users, the algorithm takes into account the inherent features of Web services, and the inherent characteristics of users when computing the similarity between services. In the aspect of QoS default prediction, in order to mitigate the influence of negative number on prediction performance, this paper improves the traditional QoS prediction algorithm based on service and the QoS prediction algorithm based on user. When the predicted QoS value for the target user is negative, the service or the user QoS arithmetic average method is used to calculate the population. Finally, the QoS prediction algorithm based on services and the QoS prediction algorithm based on users are combined to give the final QoS prediction results using the adaptive equalization weight method. In order to verify the superiority of the proposed algorithm, this paper uses a large scale QoS data set in real environment to carry out simulation experiments. The dataset contains 1500000 records of Web service calls, and the superiority of the proposed algorithm is proved by simulation and comparison experiments.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09
【參考文獻】
相關期刊論文 前8條
1 李德毅，孟海軍，史雪梅;隸屬云和隸屬云發(fā)生器[J];計算機研究與發(fā)展;1995年06期
2 馮在文;何克清;李兵;龔平;何揚帆;劉瑋;;一種基于情境推理的語義Web服務發(fā)現(xiàn)方法[J];計算機學報;2008年08期
3 鄧水光;尹建偉;李瑩;吳健;吳朝暉;;基于二分圖匹配的語義Web服務發(fā)現(xiàn)方法[J];計算機學報;2008年08期
4 張富國;徐升華;;推薦系統(tǒng)安全問題及技術研究綜述[J];計算機應用研究;2008年03期
5 鄧愛林,朱揚勇,施伯樂;基于項目評分預測的協(xié)同過濾推薦算法[J];軟件學報;2003年09期
6 岳昆,王曉玲,周傲英;Web服務核心支撐技術:研究綜述[J];軟件學報;2004年03期
7 邵凌霜;周立;趙俊峰;謝冰;梅宏;;一種Web Service的服務質(zhì)量預測方法[J];軟件學報;2009年08期
8 劉建國;周濤;汪秉宏;;個性化推薦系統(tǒng)的研究進展[J];自然科學進展;2009年01期
相關博士學位論文 前1條
1 高e
本文編號：2307634
本文鏈接：http://sikaile.net/guanlilunwen/ydhl/2307634.html
最近更新
教材專著
