本文選題：服務(wù)推薦　切入點(diǎn)：協(xié)同過(guò)濾　出處：《杭州電子科技大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的飛速發(fā)展,面向服務(wù)的體系結(jié)構(gòu)(Service Oriented Architecture,SOA)在分布式系統(tǒng)和軟件集成領(lǐng)域盛行。在這一大環(huán)境下,Web服務(wù)的數(shù)量在近年內(nèi)快速增長(zhǎng),這導(dǎo)致用戶從海量的Web服務(wù)中尋找滿足自己需求的服務(wù)愈發(fā)困難。因此,為了滿足每個(gè)用戶的需求,如何從大規(guī)模的Web服務(wù)群中選擇出具有高質(zhì)量的服務(wù)并且做出個(gè)性化推薦是一個(gè)非常具有挑戰(zhàn)性的任務(wù)。基于服務(wù)質(zhì)量(Quality of Service,QoS)的服務(wù)推薦是當(dāng)下Web服務(wù)技術(shù)領(lǐng)域的熱點(diǎn)問(wèn)題。在為用戶做出個(gè)性化推薦之前,準(zhǔn)確地預(yù)測(cè)QoS值至關(guān)重要。協(xié)同過(guò)濾(Collaborative Filtering,CF)方法在Web服務(wù)推薦系統(tǒng)中得到廣泛使用,這種方法利用用戶調(diào)用服務(wù)的歷史QoS值來(lái)分析每個(gè)用戶的偏好特征,并找出相似群體,能夠非常智能地做出推薦。然而,傳統(tǒng)的協(xié)同過(guò)濾方法沒(méi)有考慮用戶-服務(wù)之間的潛在特征,比如網(wǎng)絡(luò)位置、地理位置,這些信息對(duì)于Web服務(wù)推薦準(zhǔn)確率有著顯著的影響。此外,協(xié)同過(guò)濾算法在大規(guī)模數(shù)據(jù)稀疏的情況下,存在服務(wù)質(zhì)量預(yù)測(cè)精度不高的問(wèn)題。針對(duì)以上問(wèn)題,本文提出兩個(gè)新穎的服務(wù)推薦算法:(1)充分利用用戶-服務(wù)之間的潛在特征,提出了一種基于貝葉斯分類(lèi)的混合協(xié)同過(guò)濾服務(wù)QoS預(yù)測(cè)方法,該方法首先通過(guò)用戶-服務(wù)的歷史QoS提取出用戶服務(wù)的特征如用戶經(jīng)度、緯度以及服務(wù)的提供商以及地區(qū)編號(hào),然后基于提取出的特征使用樸素貝葉斯算法對(duì)用戶-服務(wù)進(jìn)行分類(lèi),最后使用基于混合的協(xié)同過(guò)濾算法在目標(biāo)用戶分類(lèi)中找出目標(biāo)用戶最相似的用戶對(duì)目標(biāo)服務(wù)的QoS值進(jìn)行預(yù)測(cè),從而提高了預(yù)測(cè)準(zhǔn)確度;(2)針對(duì)協(xié)同過(guò)濾算法預(yù)測(cè)準(zhǔn)確度受限于相似用戶選擇準(zhǔn)確度的問(wèn)題,提出了一種基于DBSCAN共現(xiàn)矩陣的相似用戶選擇方法,提高了相似用戶的選擇準(zhǔn)確度。并且針對(duì)分類(lèi)器的分類(lèi)準(zhǔn)確率受限于目標(biāo)特征向量的有效性,提出了用戶和服務(wù)的頻次向量特征,該特征向量能夠顯著標(biāo)識(shí)用戶-服務(wù)的個(gè)性特征,提高了AdaBoost分類(lèi)器的分類(lèi)準(zhǔn)確度。根據(jù)分類(lèi)器輸出結(jié)果的概率近鄰模型,并進(jìn)一步提出了一種聚合模型,該聚合模型綜合上述兩個(gè)概率近鄰模型的結(jié)果,提高了預(yù)測(cè)準(zhǔn)確度。本文分別使用提出的兩種方法在真實(shí)的數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),并且與一些著名方法進(jìn)行了比較,實(shí)驗(yàn)結(jié)果證明,本文的兩種方法準(zhǔn)確度均有提升。
[Abstract]:With the rapid development of Internet technology, Service Oriented Architecture SOA (Service Oriented Architecture SOA) is popular in the field of distributed systems and software integration. This makes it more difficult for users to find services that meet their needs from a large number of Web services. How to select high quality service from large scale Web service cluster and make personalized recommendation is a very challenging task. Service recommendation based on quality of Service quality is the technical field of Web service. Before making personalized recommendations for users, It is very important to accurately predict the QoS value. Collaborative filtering Collaborative filtering method is widely used in Web service recommendation system. This method uses the historical QoS value of the user calling service to analyze the preference characteristics of each user and to find out the similar group. Be able to make recommendations very intelligently. However, traditional collaborative filtering methods do not take into account potential features between users and services, such as network location, geographic location, This information has a significant impact on the accuracy of Web service recommendation. In addition, the collaborative filtering algorithm has the problem of low quality of service prediction accuracy when large scale data is sparse. In this paper, two novel service recommendation algorithms: 1) are proposed to make full use of the potential features between users and services, and a hybrid collaborative filtering service QoS prediction method based on Bayesian classification is proposed. In this method, the features of user service, such as user longitude, latitude, service provider and region number, are extracted by the historical QoS of user-service. Then, based on the extracted features, a naive Bayesian algorithm is used to classify the user-service. Finally, a hybrid collaborative filtering algorithm is used to find out the most similar users in the classification of target users to predict the QoS value of the target service. To solve the problem that the prediction accuracy of collaborative filtering algorithm is limited by similar user selection accuracy, a similar user selection method based on DBSCAN co-occurrence matrix is proposed. The classification accuracy of the classifier is limited by the effectiveness of the target feature vector, and the frequency vector feature of the user and service is proposed. The feature vector can clearly identify the personality characteristics of the user-service and improve the classification accuracy of the AdaBoost classifier. According to the probability nearest neighbor model of the classifier output result, a aggregation model is proposed. This aggregation model synthesizes the results of the two probabilistic nearest neighbor models mentioned above and improves the prediction accuracy. In this paper, we use the two proposed methods to carry out experiments on real data sets, and compare them with some famous methods. The experimental results show that the accuracy of the two methods is improved.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP393.09;TP391.3

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本文編號(hào)：1574636