發(fā)布時(shí)間：2018-06-27 14:14

  本文選題：協(xié)同過濾 + 數(shù)據(jù)平滑機(jī)制�。� 參考：《浙江大學(xué)》2013年碩士論文

【摘要】：隨著云計(jì)算的發(fā)展,越來越多的應(yīng)用以云端服務(wù)的形式開放,隨之引發(fā)了Web服務(wù)數(shù)量的爆炸式增長(zhǎng),互聯(lián)網(wǎng)上涌現(xiàn)出越來越多的功能相同但服務(wù)質(zhì)量(QoS)不同的Web服務(wù)。面對(duì)如此龐大的服務(wù)集合,用戶手工在服務(wù)注冊(cè)中心或者搜索引擎上查找所需服務(wù)變得越來越困難�；赒oS的服務(wù)推薦,旨在從眾多等功能服務(wù)中挑選出滿足用戶QoS需求的服務(wù),已經(jīng)成為服務(wù)計(jì)算(SOC)領(lǐng)域最炙手可熱的研究方向之一。 以往的研究工作已經(jīng)充分利用了候選服務(wù)的QoS屬性來解決此問題,然而這些工作都是基于一個(gè)共同的前提,那就是假設(shè)所有候選Web服務(wù)針對(duì)目標(biāo)用戶的QoS值均已知。由于Web服務(wù)的QoS具有不確定性(同一個(gè)Web服務(wù)針對(duì)不同用戶的QoS存在很大差異)以及不完整性(很少有用戶曾調(diào)用過所有的候選Web服務(wù)),因而這種假設(shè)和實(shí)際應(yīng)用的情況存在相當(dāng)大的差距,換而言之,很多Web服務(wù)針對(duì)目標(biāo)用戶的QoS是未知的。 為解決QoS值缺失的問題,本文基于協(xié)同過濾的思想,提出一種創(chuàng)新的QoS預(yù)測(cè)算法DRaC。DRaC算法中引入了數(shù)據(jù)平滑機(jī)制,對(duì)訓(xùn)練集中的用戶進(jìn)行聚類操作,并利用各聚類中用戶的歷史QoS信息對(duì)預(yù)測(cè)系統(tǒng)的輸入數(shù)據(jù)集進(jìn)行數(shù)據(jù)平滑化預(yù)處理,可以有效提高系統(tǒng)的QoS預(yù)測(cè)準(zhǔn)確度。不同于傳統(tǒng)的基于協(xié)同過濾的預(yù)測(cè)算法,DRaC算法提出了反向交叉預(yù)測(cè)方法,充分合理利用了訓(xùn)練矩陣中相似度較低的用戶與服務(wù)數(shù)據(jù),可改善數(shù)據(jù)稀疏問題為預(yù)測(cè)系統(tǒng)帶來的影響,優(yōu)化了預(yù)測(cè)效果。此外DRaC算法提出基于用戶反饋的信任度模型,在線統(tǒng)計(jì)學(xué)習(xí)用戶對(duì)系統(tǒng)推薦結(jié)果的反饋信息,自動(dòng)建立與維護(hù)用戶信任度模型,并將其與QoS預(yù)測(cè)過程相結(jié)合,可以做到動(dòng)態(tài)改善系統(tǒng)的預(yù)測(cè)效果。 最后,本文基于真實(shí)的QoS數(shù)據(jù)集驗(yàn)證了DRaC預(yù)測(cè)算法的效果,并通過實(shí)驗(yàn)分析了算法中各個(gè)參數(shù)對(duì)預(yù)測(cè)結(jié)果的影響。
[Abstract]:With the development of cloud computing, more and more applications are open in the form of cloud services, which has triggered an explosive increase in the number of Web services. More and more Web services with the same function but different quality of service (QoS) are emerging on the Internet. It is becoming more and more difficult to find the services needed. QoS based service recommendation, which is designed to select services that meet the user's QoS requirements from many other functional services, has become one of the hottest research directions in the service computing (SOC) field.
Previous research has made full use of the QoS attributes of candidate services to solve this problem. However, these work is based on a common premise that all candidate Web services are known for the QoS value of the target users. Because the QoS of the Web service is uncertain (the same Web service exists for the QoS of different users. There are great differences) and incompleteness (few users have ever called all candidate Web services), so there is a considerable gap between this hypothesis and the actual application, in other words, a lot of Web services are unknown to the target user's QoS.
In order to solve the problem of missing QoS value, based on the idea of collaborative filtering, this paper proposes an innovative QoS prediction algorithm DRaC.DRaC algorithm, which introduces a data smoothing mechanism to cluster the trained users, and uses the historical QoS information of the users in each cluster to preprocess the data set of the input data set of the prediction system. It can effectively improve the QoS prediction accuracy. Unlike the traditional collaborative filtering based prediction algorithm, the DRaC algorithm proposes a reverse cross prediction method, which makes full use of the low similarity of users and service data in the training matrix, and improves the impact of the data sparse problem to the prediction system and optimizes the prediction effect. In addition, the DRaC algorithm proposes a trust degree model based on user feedback. It can learn the feedback information of the user's recommendation results online, and automatically establish and maintain the user trust model, and combine it with the QoS prediction process, and can improve the prediction effect of the system dynamically.
Finally, based on the real QoS dataset, the effectiveness of the DRaC prediction algorithm is verified, and the influence of the parameters in the algorithm on the prediction results is analyzed through experiments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP393.09;TP391.3

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本文編號(hào)：2074182