本文選題：不信任　切入點(diǎn)：信任　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著網(wǎng)絡(luò)技術(shù)的不斷發(fā)展人們已經(jīng)從一個(gè)信息十分匱乏的時(shí)代過(guò)渡到信息量巨大的信息海洋世界里,面對(duì)如此龐大的信息數(shù)據(jù),人們想要從中快速的獲取自己想要的信息是十分困難的事情。由此推薦系統(tǒng)應(yīng)運(yùn)而生,能夠提供給人們個(gè)性化的網(wǎng)絡(luò)服務(wù),使得人們?cè)跒g覽網(wǎng)頁(yè)的時(shí)候能快速精準(zhǔn)的定位自己需要的信息。推薦系統(tǒng)最早應(yīng)用在電子商務(wù)領(lǐng)域,在用戶購(gòu)買物品的時(shí)候能夠得到一個(gè)良好的推薦,提升了客戶體驗(yàn)度。但由于推薦系統(tǒng)的數(shù)據(jù)稀疏性和冷啟動(dòng)等問(wèn)題的困擾使得一些用戶并不能得到很好的推薦。由此一種基于社交網(wǎng)絡(luò)的推薦系統(tǒng)誕生了。這種算法將社交網(wǎng)絡(luò)中的信任關(guān)系考慮在推薦系統(tǒng)當(dāng)中,具體的結(jié)合方式有多種。比如可以用信任關(guān)系篩選信任用戶形成信任用戶-項(xiàng)目評(píng)分矩陣,計(jì)算相似度篩選最鄰近;而有一些是把相似度與信任值相結(jié)合形成一個(gè)新的綜合值來(lái)篩選最鄰近等。無(wú)論以上哪種方式都對(duì)推薦系統(tǒng)的效率和準(zhǔn)確率有了明顯的改善和提高,同時(shí)也使得長(zhǎng)尾效應(yīng)的影響變得更加小了。但是我們也知道,社交網(wǎng)絡(luò)是一個(gè)十分復(fù)雜的社會(huì)關(guān)系網(wǎng),其中不僅僅有信任關(guān)系,還有許多不信任關(guān)系等因素,因此在推薦算法中將不信任因素考慮進(jìn)去也是理所應(yīng)當(dāng)?shù)氖虑�。雖然現(xiàn)有階段已有一些國(guó)外的學(xué)者討論了這方面的問(wèn)題,但都不夠全面,沒(méi)有給出一個(gè)完整的不信任模型,并且沒(méi)有很好的解決不信任關(guān)系網(wǎng)絡(luò)的數(shù)據(jù)稀疏性問(wèn)題。為了解決上述問(wèn)題,使得能有一個(gè)更好的個(gè)性化推薦算法服務(wù)于用戶,更好的解決數(shù)據(jù)稀疏性問(wèn)題和考慮方面過(guò)于單一化問(wèn)題。本文的主要工作如下:提出了一個(gè)全新的基于廣度優(yōu)先遍歷的不信任關(guān)系傳遞算法。本文提出了不信任的傳遞算法,用以拓展社交網(wǎng)絡(luò)中不信任關(guān)系網(wǎng)絡(luò)。為了實(shí)現(xiàn)以上提出的基于廣度優(yōu)先遍歷的不信任關(guān)系傳遞算法,本文設(shè)計(jì)了不信任關(guān)系傳遞模型,使得不信任關(guān)系得以量化。本文將基于廣度優(yōu)先遍歷的不信任關(guān)系傳遞算法與基于信任的協(xié)同過(guò)濾算法相結(jié)合,產(chǎn)生了一個(gè)全新的基于廣度優(yōu)先遍歷的不信任網(wǎng)絡(luò)擴(kuò)展推薦算法。該算法顯著的解決了社交網(wǎng)絡(luò)中不信任矩陣稀疏性的問(wèn)題,同時(shí)也兼顧緩解了推薦系統(tǒng)稀疏性的問(wèn)題,在總體上提高了推薦系統(tǒng)的準(zhǔn)確性和效率。以Epinions為數(shù)據(jù)集進(jìn)行實(shí)驗(yàn)驗(yàn)證,并與傳統(tǒng)的協(xié)同過(guò)濾推薦算法進(jìn)行分析對(duì)比,證實(shí)了新算法在推薦效果上要優(yōu)于傳統(tǒng)的協(xié)同過(guò)濾推薦算法。
[Abstract]:With the continuous development of network technology, people have been transitioning from an era of very scarce information to a world of information ocean with huge amount of information, faced with such huge information data. It is very difficult for people to get the information they want quickly from it. So that people can quickly and accurately locate the information they need when they browse the web. Recommendation system was first applied in the field of electronic commerce, and it can get a good recommendation when users buy items. But because of the problems of data sparsity and cold startup of recommendation system, some users are not able to get good recommendation. Thus, a recommendation system based on social network was born. The algorithm considers the trust relationship in the social network as a recommendation system. For example, trust relationship can be used to filter trust users to form trust user-item scoring matrix, and to calculate similarity filtering nearest to each other. Some of them combine similarity and trust to form a new comprehensive value to select the nearest neighbor. Either way, the efficiency and accuracy of the recommendation system have been improved and improved. But we also know that social networks are a very complex social network, which has not only trust relationships, but also many factors such as distrust. Therefore, it is natural to take the distrust factor into account in the recommendation algorithm. Although some foreign scholars have discussed this problem at the present stage, it is not comprehensive enough to give a complete model of distrust. In order to solve the above problem, there is a better personalized recommendation algorithm to serve users. The main work of this paper is as follows: a new algorithm based on breadth-first traversal for the transfer of distrust relationship is proposed. In order to realize the above proposed algorithm based on breadth-first traversal, this paper designs a model of distrust relation transfer. In this paper, we combine the breadth-first traversal of the distrust transfer algorithm with the trust based collaborative filtering algorithm. A new extended recommendation algorithm based on breadth-first traversing is presented. The algorithm solves the problem of the sparsity of distrust matrix in social networks and also alleviates the sparse problem of recommendation system. On the whole, the accuracy and efficiency of the recommendation system are improved. The experimental results are verified by using Epinions as the data set, and compared with the traditional collaborative filtering recommendation algorithm. It is proved that the new algorithm is superior to the traditional collaborative filtering recommendation algorithm in recommendation effect.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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