【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的不斷發(fā)展，Web服務(wù)推薦與選擇已經(jīng)逐漸成為學(xué)術(shù)界和工業(yè)界共同關(guān)注的重要研究?jī)?nèi)容,服務(wù)質(zhì)量(QoS)是成功進(jìn)行Web服務(wù)推薦的關(guān)鍵性因素。然而，，Web服務(wù)的QoS值在運(yùn)行時(shí)刻可能會(huì)因?yàn)榉⻊?wù)器超載，網(wǎng)絡(luò)條件等多種因素的影響而發(fā)生變化。由于Web服務(wù)環(huán)境的動(dòng)態(tài)性，目前現(xiàn)有的服務(wù)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(wù)推薦結(jié)果與實(shí)際結(jié)果偏差較大。為解決Web服務(wù)的QoS值的動(dòng)態(tài)性以及目前算法忽視QoS內(nèi)在的不確定性,導(dǎo)致服務(wù)選擇可靠性差問題，本文提出一種改進(jìn)的基于協(xié)同過濾的Web服務(wù)推薦方法，該方法的引入使得服務(wù)用戶不需要對(duì)Web服務(wù)進(jìn)行調(diào)用，只需要對(duì)歷史的Web服務(wù)的QoS信息進(jìn)行分析挖掘就能找出適合用戶的最優(yōu)Web服務(wù)。 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(gè)方面：在服務(wù)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(dǎo)致的服務(wù)選擇可靠性差問題，把不可靠的服務(wù)剔除；在相似度計(jì)算方面，本文算法在計(jì)算用戶間相似度時(shí)，充分考慮了Web服務(wù)的內(nèi)在特征，在計(jì)算服務(wù)間相似度時(shí)，充分考慮了用戶的內(nèi)在特征；在對(duì)QoS缺省值預(yù)測(cè)方面，為了緩解負(fù)數(shù)對(duì)預(yù)測(cè)性能的影響，本文對(duì)傳統(tǒng)的基于服務(wù)的QoS預(yù)測(cè)算法和基于用戶的QoS預(yù)測(cè)算法進(jìn)行改進(jìn)；當(dāng)為目標(biāo)用戶預(yù)測(cè)的QoS值為負(fù)數(shù)時(shí)，使用服務(wù)或者用戶QoS值算術(shù)平均的方法進(jìn)行計(jì)算填充。最后聯(lián)合基于服務(wù)的QoS預(yù)測(cè)算法和基于用戶的QoS預(yù)測(cè)算法采用自適應(yīng)均衡權(quán)重的方法給出最終的QoS預(yù)測(cè)結(jié)果。為驗(yàn)證本文提出算法的優(yōu)越性，本文使用了真實(shí)環(huán)境下大規(guī)模的QoS數(shù)據(jù)集進(jìn)行仿真實(shí)驗(yàn)，該數(shù)據(jù)集包含了1500000條Web服務(wù)調(diào)用記錄，通過仿真對(duì)比實(shí)驗(yàn)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(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

【參考文獻(xiàn)】

相關(guān)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(jì)算機(jī)研究與發(fā)展;1995年06期

2 馮在文;何克清;李兵;龔平;何揚(yáng)帆;劉瑋;;一種基于情境推理的語(yǔ)義Web服務(wù)發(fā)現(xiàn)方法[J];計(jì)算機(jī)學(xué)報(bào);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(yǔ)義Web服務(wù)發(fā)現(xiàn)方法[J];計(jì)算機(jī)學(xué)報(bào);2008年08期

4 張富國(guó);徐升華;;推薦系統(tǒng)安全問題及技術(shù)研究綜述[J];計(jì)算機(jī)應(yīng)用研究;2008年03期

5 鄧愛林,朱揚(yáng)勇,施伯樂;基于項(xiàng)目評(píng)分預(yù)測(cè)的協(xié)同過濾推薦算法[J];軟件學(xué)報(bào);2003年09期

6 岳昆,王曉玲,周傲英;Web服務(wù)核心支撐技術(shù):研究綜述[J];軟件學(xué)報(bào);2004年03期

7 邵凌霜;周立;趙俊峰;謝冰;梅宏;;一種Web Service的服務(wù)質(zhì)量預(yù)測(cè)方法[J];軟件學(xué)報(bào);2009年08期

8 劉建國(guó);周濤;汪秉宏;;個(gè)性化推薦系統(tǒng)的研究進(jìn)展[J];自然科學(xué)進(jìn)展;2009年01期

相關(guān)博士學(xué)位論文 前1條

1 高e

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