【摘要】：近年來,隨著互聯(lián)網(wǎng)迅速發(fā)展和移動(dòng)設(shè)備的普及,互聯(lián)網(wǎng)上出現(xiàn)了大量功能相似、種類繁多的Web服務(wù)。如何向用戶推薦個(gè)性化的Web服務(wù)已成為服務(wù)計(jì)算領(lǐng)域的熱點(diǎn)研究問題之一。傳統(tǒng)的Web服務(wù)推薦算法已無法滿足Web服務(wù)推薦的多元化需求。因此,考慮時(shí)間、空間等情境因素的情境感知Web服務(wù)推薦方法應(yīng)運(yùn)而生。然而,現(xiàn)有的情境感知Web服務(wù)推薦算法的研究仍存在如下問題:第一,現(xiàn)有算法主要關(guān)注于利用空間、時(shí)間等情境信息來尋找與當(dāng)前用戶的情境相似的用戶或與當(dāng)前Web服務(wù)的情境相似的服務(wù)。然而,尚未充分考慮用戶情境和Web服務(wù)情境之間的關(guān)聯(lián)性對(duì)用戶Web服務(wù)偏好產(chǎn)生的影響因素。因此,Web服務(wù)的推薦結(jié)果難以響應(yīng)用戶或服務(wù)情境的動(dòng)態(tài)變化。第二,在使用“用戶-服務(wù)”Qo S值矩陣進(jìn)行用戶之間或服務(wù)之間相似度計(jì)算時(shí),現(xiàn)有方案將大小不同的QoS值同等看待,而普遍忽略了極大或極小的QoS值對(duì)用戶之間的相似度或服務(wù)之間的相似度的顯著影響。因而,難以為用戶推薦顯著反映用戶偏好的服務(wù)。針對(duì)上述問題,本文提出了一種面向空間相關(guān)性和加權(quán)評(píng)分效應(yīng)的情境感知Web服務(wù)推薦方法(簡(jiǎn)稱CASR-SCWRE算法)。該方法旨在為用戶提供一種個(gè)性化的Web服務(wù)推薦機(jī)制:一方面,有利于挖掘用戶情境和服務(wù)情境之間的相關(guān)性對(duì)用戶Web服務(wù)偏好的影響,另一方面,保證推薦算法能夠顯著反映不同Qo S值對(duì)用戶相似度或服務(wù)相似度的顯著影響。本文的主要研究?jī)?nèi)容包括:首先,通過用戶空間情境和服務(wù)空間情境之間的關(guān)聯(lián)性,建模了空間相關(guān)性對(duì)用戶Web服務(wù)偏好影響,得到與用戶當(dāng)前偏好相符的Web服務(wù)調(diào)用記錄。其次,通過建模加權(quán)評(píng)分效應(yīng)來計(jì)算用戶之間相似度或服務(wù)之間相似度,并結(jié)合傳統(tǒng)的時(shí)間衰減相似度求解模型,提出了基于加權(quán)評(píng)分效應(yīng)的時(shí)間衰減模型。再次,基于上述兩步過濾得到的數(shù)據(jù),利用貝葉斯定理預(yù)測(cè)某一特定的Web服務(wù)對(duì)當(dāng)前用戶的QoS值。最后,本文在WS-Dream數(shù)據(jù)集上開展了一系列大規(guī)模的實(shí)驗(yàn)訓(xùn)練和測(cè)試。實(shí)驗(yàn)結(jié)果表明,CASR-SCWRE算法與諸多對(duì)比算法相比,具有更高的Web服務(wù)推薦精度。
文內(nèi)圖片：
圖片說明：天氣預(yù)報(bào)Web服務(wù)推薦場(chǎng)景圖
[Abstract]:In recent years, with the rapid development of the Internet and the popularity of mobile devices, a large number of similar functions and a wide range of Web services have appeared on the Internet. How to recommend personalized Web services to users has become one of the hot research issues in the field of service computing. The traditional Web service recommendation algorithm can not meet the diversified requirements of Web service recommendation. Therefore, context-aware Web service recommendation method considering time, space and other situational factors emerges as the times require. However, the existing context-aware Web service recommendation algorithms still have the following problems: first, the existing algorithms mainly focus on using space, time and other situational information to find users who are similar to the current user's situation or similar to the current Web service situation. However, the relationship between user situation and Web service situation has not yet been fully considered, which affects the preference of user Web service. Therefore, the recommended results of Web services are difficult to respond to the dynamic changes of users or service situations. Secondly, when using the "user-service" QoS value matrix to calculate the similarity between users or between services, the existing schemes treat the QoS values with different sizes equally, and generally ignore the significant influence of the maximum or minimal QoS values on the similarity between users or between services. Therefore, it is difficult to recommend services that significantly reflect the preferences of users. In order to solve the above problems, a context-aware Web service recommendation method (CASR-SCWRE algorithm) for spatial correlation and weighted scoring effect is proposed in this paper. The purpose of this method is to provide users with a personalized Web service recommendation mechanism: on the one hand, it is beneficial to mine the influence of the correlation between user situation and service situation on users' Web service preference, on the other hand, it ensures that the recommendation algorithm can significantly reflect the significant influence of different Qo S values on user similarity or service similarity. The main research contents of this paper are as follows: firstly, through the correlation between user space situation and service space situation, the influence of spatial correlation on user Web service preference is modeled, and the Web service call record consistent with user's current preference is obtained. Secondly, the similarity between users or services is calculated by modeling weighted scoring effect, and combined with the traditional time attenuation similarity solution model, a time attenuation model based on weighted scoring effect is proposed. Thirdly, based on the data filtered in the above two steps, the QoS value of a particular Web service to the current user is predicted by using Bayesian theorem. Finally, a series of large-scale experimental training and testing are carried out on WS-Dream datasets. The experimental results show that the CASR-SCWRE algorithm has higher accuracy of Web service recommendation than many comparative algorithms.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09;TP391.3

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