【摘要】：Web的快速發(fā)展帶來了信息爆炸的現(xiàn)狀,現(xiàn)在對個性化Web服務(wù)推薦信息系統(tǒng)的研究成為服務(wù)計算領(lǐng)域的一個熱門研究方向。Web服務(wù)推薦系統(tǒng)的研究主要解決兩個問題:稀疏QoS數(shù)據(jù)的預(yù)測及補(bǔ)全,用戶個性化推薦。問題一是因為提供相同或相似功能的Web服務(wù)數(shù)量很多,對于個體用戶而言,很有可能被推薦的服務(wù)是該用戶從未接觸過的,這樣就存在QoS數(shù)據(jù)的稀疏問題,這就需要對缺失的數(shù)據(jù)進(jìn)行補(bǔ)全。問題二是因為推薦給用戶的服務(wù)是不是準(zhǔn)確不能僅僅通過QoS值的預(yù)測來判斷,還要考慮用戶的個性化需求,在考慮用戶的個性化需求的條件下對稀疏QoS值進(jìn)行預(yù)測和補(bǔ)全,才能推薦合適的服務(wù)給目標(biāo)用戶�；趨f(xié)同過濾的推薦系統(tǒng)能根據(jù)相似用戶或相似服務(wù)的評分來預(yù)測當(dāng)前用戶的評分,在該研究方向已經(jīng)有不少研究成果,但是QoS數(shù)據(jù)預(yù)測的準(zhǔn)確性和個性化推薦的合理行方面仍然存在很多不足。在基于協(xié)同過濾的Web服務(wù)推薦算法研究方向,本文提出以下三種改進(jìn)的算法模型:首先,本文提出了一種基于用戶偏好的改進(jìn)協(xié)同過濾Web服務(wù)推薦算法(UPCF),該算法的基本思路是,首先從QoS數(shù)據(jù)中提取用戶偏好,并將其作為相似用戶的選擇標(biāo)準(zhǔn),然后使用top-k算法確定目標(biāo)用戶及服務(wù)的相似鄰居,最后使用調(diào)整的加權(quán)和方法來預(yù)測目標(biāo)用戶的QoS值。其次,本文在基于用戶偏好的改進(jìn)協(xié)同過濾Web服務(wù)推薦算法(UPCF)基礎(chǔ)上,提出了基于聯(lián)合用戶偏好的改進(jìn)協(xié)同過濾Web服務(wù)推薦算法(CUPCF)。該算法從QoS數(shù)據(jù)中提取用戶偏好數(shù)據(jù)并使用于相似鄰居的選擇,在使用top-k算法確定目標(biāo)用戶及服務(wù)的相似鄰居集合之后,使用QoS數(shù)據(jù)計算鄰居的相似度,最后使用調(diào)整的加權(quán)和方法來預(yù)測目標(biāo)用戶的QoS值。最后,本文在CUPCF算法的基礎(chǔ)上,提出了基于用戶位置與偏好的改進(jìn)協(xié)同過濾Web服務(wù)推薦算法(LSCUPCF),該算法將QoS數(shù)據(jù)按照用戶的位置分布劃分為幾個子類,子類中的用戶由于位置的相似具有更好的相似性,LSCUPCF在提高用戶個性化考慮的基礎(chǔ)上,降低了算法的計算復(fù)雜度,提高了算法的效率及準(zhǔn)確率。我們的實驗使用香港中文大學(xué)發(fā)布的WSDREAM數(shù)據(jù)集,該數(shù)據(jù)集收集了全球30個國家的339個用戶和70多個國家的5825個Web服務(wù),包含197萬條真實Web服務(wù)QoS訪問記錄,WSDREAM數(shù)據(jù)集上的實驗結(jié)果表明本文所提出的一系列推薦算法具有更好的預(yù)測準(zhǔn)確率。
[Abstract]:The rapid development of Web has brought about the status quo of information explosion. Now the research on personalized Web services recommendation information system has become a hot research direction in the field of service computing. The research of web services recommendation system mainly solves two problems: sparse QoS data prediction and completion, user personalized recommendation. The first problem is that there are so many Web services that provide the same or similar functions that for an individual user, it is likely that the recommended service has never been in contact with that user, so there is a problem of sparse QoS data. This requires the completion of missing data. The second problem is that whether the service recommended to the user can not be judged by the prediction of the QoS value, but also by the consideration of the personalized demand of the user, and the sparse QoS value can be predicted and completed under the condition of considering the user's personalized demand. To recommend appropriate services to target users. The recommendation system based on collaborative filtering can predict the current users' scores according to the scores of similar users or similar services. However, there are still many shortcomings in the accuracy of QoS data prediction and the reasonable line of personalized recommendation. In the research direction of Web services recommendation algorithm based on collaborative filtering, this paper proposes the following three improved algorithm models: first, this paper proposes an improved collaborative filtering Web service recommendation algorithm based on user preference, (UPCF), the basic idea of the algorithm is: First, the user preference is extracted from the QoS data and used as the selection criterion for the similar users, then the top-k algorithm is used to determine the similar neighbors of the target user and the service. Finally, the adjusted weighted sum method is used to predict the QoS value of the target user. Secondly, based on the improved collaborative filtering Web service recommendation algorithm (UPCF) based on user preference, this paper proposes an improved collaborative filtering Web service recommendation algorithm (CUPCF). Based on joint user preference. The algorithm extracts user preference data from QoS data and uses it to select similar neighbors. After using top-k algorithm to determine the set of similar neighbors of target users and services, QoS data is used to calculate the similarity of neighbors. Finally, the adjusted weighted sum method is used to predict the target user's QoS value. Finally, based on the CUPCF algorithm, an improved collaborative filtering Web service recommendation algorithm based on user location and preference, (LSCUPCF), is proposed. The algorithm divides the QoS data into several subclasses according to the user's location distribution. The users in the subclass have better similarity because of the similarity of location. LSCUPCF reduces the computational complexity of the algorithm and improves the efficiency and accuracy of the algorithm on the basis of improving the personalized consideration of users. Our experiment uses the WSDREAM dataset released by the Chinese University of Hong Kong, which collects 339 users in 30 countries and 5825 Web services in more than 70 countries. The experimental results on the WSDREAM dataset containing 1.97 million real Web service QoS access records show that the proposed algorithms have better prediction accuracy.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3;TP393.09

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