【摘要】：隨著Web服務(wù)數(shù)量的迅速增長,面對海量的Web服務(wù),構(gòu)建高效的Web服務(wù)推薦系統(tǒng)很有必要。為了向用戶推薦高質(zhì)量的服務(wù),關(guān)鍵問題是如何獲得Web服務(wù)的Qo S值。盡管用戶可以通過親自調(diào)用Web服務(wù)來評估它的Qo S,但是由于服務(wù)的用戶并不是評價服務(wù)的專家,要在短時間對大量候選服務(wù)的Qo S進行準(zhǔn)確評估是不太現(xiàn)實的。考慮到Web服務(wù)的Qo S值是與具體用戶相關(guān)的,近年來不少工作利用協(xié)同過濾推薦技術(shù)來進行個性化的Qo S預(yù)測和服務(wù)推薦,取得了一定的成效。然而,傳統(tǒng)的協(xié)同過濾技術(shù)在應(yīng)用時受數(shù)據(jù)稀疏性的影響較大,且存在冷啟動以及可擴展性差等問題。此外,考慮到網(wǎng)絡(luò)延遲和網(wǎng)絡(luò)條件,同一個地區(qū)的用戶有較大可能在相同Web服務(wù)上觀察到相似的響應(yīng)時間。針對以往基于協(xié)同過濾的Web服務(wù)推薦方法的不足,本文提出了一種新的Web服務(wù)Qo S預(yù)測及推薦方法。本文的主要貢獻如下:(1)提出了一種基于位置聚類的協(xié)同式Web服務(wù)推薦方法,該方法首先利用服務(wù)Qo S與用戶位置的相關(guān)性,將用戶根據(jù)自治系統(tǒng)(國家)進行聚類,并根據(jù)聚類結(jié)果對空缺Qo S值進行填充;然后再對空缺Qo S值預(yù)先進行填充和計算活動用戶與各個用戶相似度的基礎(chǔ)上,利用To P-K算法,求得最相似來為活動用戶預(yù)測未知服務(wù)的Qo S值,完成推薦。我們的方法能夠有效解決Web服務(wù)數(shù)據(jù)稀疏性問題和冷啟動問題,同時,在精度和覆蓋率之間獲得一個更好的平衡。為了更好的驗證我們所提出的方法的準(zhǔn)確性,我們將該方法在真實的Web服務(wù)數(shù)據(jù)集上進行了一系列全面的實驗,結(jié)果顯示了所提方法的優(yōu)越性。(2)提出了一種基于因子分解機的質(zhì)量感知Web服務(wù)推薦方法,本文利用Web服務(wù)的特點,將用戶和服務(wù)的網(wǎng)絡(luò)位置信息和因子分解機相結(jié)合,提出了一種位置感知的因子分解機模型及相應(yīng)的Web服務(wù)推薦方法。該方法根據(jù)位置信息確定用戶和服務(wù)的相似鄰居集合,然后顯式地利用相似用戶和相似服務(wù)信息改進因子分解機模型,以準(zhǔn)確預(yù)測未知Web服務(wù)的質(zhì)量和推薦高質(zhì)量的Web服務(wù)。該方法使用了在真實數(shù)據(jù)集上的實驗表明該算法在預(yù)測精度上優(yōu)于其它協(xié)同過濾式推薦算法。同時該算法具有較高的運行效率,預(yù)測服務(wù)質(zhì)量的時間復(fù)雜度與數(shù)據(jù)規(guī)模的大小呈線性相關(guān),可以較好地解決大規(guī)模推薦系統(tǒng)的數(shù)據(jù)稀疏性與可擴展性問題。
[Abstract]:With the rapid growth of Web services, it is necessary to build an efficient Web services recommendation system in the face of massive Web services. In order to recommend high quality service to users, the key problem is how to get the Qo S value of Web service. Although the user can evaluate the Web service by calling it himself, it is not realistic to evaluate the Qo S of a large number of candidate services in a short time because the user of the service is not an expert in evaluating the service. Considering that the Qo S value of Web services is related to specific users, in recent years, a lot of work has made use of collaborative filtering recommendation technology to carry out personalized Qo S prediction and service recommendation, and achieved certain results. However, the traditional collaborative filtering technology is greatly affected by data sparsity in application, and there are some problems such as cold start and poor scalability. In addition, considering network latency and network conditions, users in the same area are more likely to observe similar response times on the same Web service. In view of the shortcomings of the previous Web service recommendation methods based on collaborative filtering, a new Web service Qo S prediction and recommendation method is proposed in this paper. The main contributions of this paper are as follows: (1) A collaborative Web service recommendation method based on location clustering is proposed. Firstly, by using the correlation between service Qo S and user location, users are clustered according to autonomous system (state). According to the clustering result, the vacant Qo S value is filled, and then the vacant Qo S value is filled in beforehand and the similarity between the active user and each user is calculated, then the To P-K algorithm is used. Obtain the most similar to predict the unknown service Qo S value for the active user, complete the recommendation. Our method can effectively solve the problem of Web service data sparsity and cold start, and achieve a better balance between precision and coverage. In order to better verify the accuracy of the proposed method, we conducted a series of comprehensive experiments on the real Web services data set. The results show the superiority of the proposed method. (2) A quality-aware Web service recommendation method based on factorizer is proposed. This paper combines the network location information of user and service with the factoring machine by using the characteristics of Web service. This paper presents a location-aware factoring machine model and a corresponding Web service recommendation method. This method determines the set of similar neighbors of users and services according to location information, and then explicitly uses similar users and similar service information to improve the factoring machine model to accurately predict the quality of unknown Web services and recommend high-quality Web services. Experiments on real data sets show that the proposed algorithm is superior to other collaborative filtering recommendation algorithms in prediction accuracy. At the same time, the algorithm has high running efficiency, and the time complexity of prediction quality of service is linearly related to the size of data, which can solve the problem of data sparsity and scalability in large-scale recommendation systems.
【學(xué)位授予單位】：湖南科技大學(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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