本文選題：位置近鄰 + 聚類　； 參考：《重慶大學(xué)》2016年碩士論文

【摘要】：隨著SOA架構(gòu)和Web服務(wù)相關(guān)標(biāo)準(zhǔn)的日趨成熟,全球越來越多的開發(fā)者、組織和企業(yè)成為Web服務(wù)提供商,在各Web服務(wù)平臺上開發(fā)和提供功能各異的Web服務(wù)。這使得各平臺上Web服務(wù)數(shù)量急劇增加,目前網(wǎng)絡(luò)上海量的Web服務(wù)中,相似甚至相同功能的Web服務(wù)很多,如何在眾多功能相似的Web服務(wù)中發(fā)現(xiàn)最能滿足用戶需求的服務(wù)并推薦給相應(yīng)用戶成為一個(gè)難題�；诜⻊�(wù)質(zhì)量(Quality of Service,QoS)的Web服務(wù)推薦技術(shù)可根據(jù)服務(wù)的非功能屬性為用戶推薦最合適的Web服務(wù),已成為近年來服務(wù)計(jì)算領(lǐng)域的研究熱點(diǎn)。其中,準(zhǔn)確預(yù)測缺失的QoS屬性值是一個(gè)難點(diǎn),目前的QoS屬性值預(yù)測算法大多只根據(jù)用戶的服務(wù)調(diào)用歷史,采用協(xié)同過濾算法進(jìn)行預(yù)測,還存在預(yù)測準(zhǔn)確率不高的問題。為解決該問題,本文對基于服務(wù)質(zhì)量的Web服務(wù)推薦系統(tǒng)展開研究,將位置屬性和訪問時(shí)間上下文結(jié)合至Qo S屬性值預(yù)測之中,利用張量分解模型提高了QoS屬性值的預(yù)測準(zhǔn)確度,從而獲得更合理、有效的Web服務(wù)推薦結(jié)果。本文的主要內(nèi)容如下:(1)分析了Web服務(wù)推薦系統(tǒng)的研究背景和現(xiàn)狀,提出了本課題的主要研究內(nèi)容和創(chuàng)新點(diǎn),并對與本文主要研究內(nèi)容相關(guān)的概念和主要技術(shù)進(jìn)行深入的研究和分析,包括Web服務(wù)相關(guān)技術(shù)、協(xié)同過濾算法和張量分解模型。(2)提出了兩種基于位置和張量分解的Web服務(wù)Qo S預(yù)測算法:TATD算法和Clust TD算法。TATD算法將用戶的地理位置屬性以位置近鄰正則項(xiàng)的形式融入至張量分解模型之中,預(yù)測活躍用戶在不同時(shí)間段訪問各Web服務(wù)時(shí)的Qo S屬性值;Clust TD算法首先根據(jù)用戶和服務(wù)的位置經(jīng)緯度值將用戶和服務(wù)聚類成多個(gè)局部組,再分別對各局部組和全局的用戶、服務(wù)和時(shí)間上下文進(jìn)行張量建模和分解,最后將局部和全局張量分解的QoS預(yù)測結(jié)果進(jìn)行加權(quán)組合,考慮了用戶和服務(wù)的相對位置以及訪問時(shí)間上下文,能獲得更準(zhǔn)確的Web服務(wù)Qo S預(yù)測值。(3)在真實(shí)的Web服務(wù)訪問數(shù)據(jù)集上驗(yàn)證了本文提出的TATD算法和Clust TD算法的Qo S值預(yù)測性能,并通過Web服務(wù)個(gè)性化推薦原型系統(tǒng)的構(gòu)建對本文提出的Web服務(wù)推薦新算法進(jìn)行實(shí)踐,驗(yàn)證了新算法的可行性和有效性。
[Abstract]:With the maturity of SOA architecture and Web service related standards, more and more developers, organizations and enterprises have become Web service providers all over the world, developing and providing Web services with different functions on various Web service platforms. This makes the number of Web services on various platforms increase dramatically. At present, among the Web services in Shanghai, there are many Web services with similar or even the same functions. How to find the most suitable Web services to meet the needs of users and recommend them to the corresponding users has become a difficult problem. The Web service recommendation technology based on quality of Service (QoS) can recommend the most suitable Web service according to the non-functional attribute of the service, which has become the research hotspot in the field of service computing in recent years. It is difficult to accurately predict the missing QoS attribute value. Most of the current QoS attribute prediction algorithms are only based on the user's history of service call and use collaborative filtering algorithm to predict the missing QoS attribute value. There is still a problem of low prediction accuracy. In order to solve this problem, the Web service recommendation system based on QoS is studied in this paper. The location attribute and access time context are combined into the prediction of QoS attribute value, and the prediction accuracy of QoS attribute value is improved by using Zhang Liang decomposition model. In order to obtain more reasonable and effective Web service recommendation results. The main contents of this paper are as follows: (1) the research background and current situation of Web service recommendation system are analyzed, and the main research contents and innovation points of this subject are put forward. Furthermore, the concepts and technologies related to the main contents of this paper are deeply studied and analyzed, including the related technologies of Web services. Cooperative filtering algorithm and Zhang Liang decomposition model. 2) two Web service QoS prediction algorithms based on location and Zhang Liang decomposition are proposed, namely: TATD algorithm and Clust TD algorithm. TATD algorithm takes the geographical location attribute of user as the regular term of location nearest neighbor. Into the Zhang Liang decomposition model, In order to predict the QoS attribute value of active users visiting each Web service in different time periods, first of all, the users and services are clustered into several local groups according to the location, longitude and latitude of the user and the service, and then the local groups and the global users are treated respectively. The service and time context are modeled and decomposed by Zhang Liang. Finally, the QoS prediction results of local and global Zhang Liang decomposition are combined weighted, taking into account the relative position of the user and the service and the access time context. Can obtain more accurate Web service QoS prediction value. 3) the proposed TATD algorithm and Clust TD algorithm are verified on the real Web service access data set. The feasibility and effectiveness of the new Web services recommendation algorithm are verified by the construction of the Web services personalized recommendation prototype system.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP393.09;TP391.3

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本文編號：1875340