【摘要】：在過去近十年的時(shí)間里,許多研究人員為解決傳統(tǒng)推薦系統(tǒng)所面臨的問題以及提高推薦質(zhì)量,將信任加入到推薦當(dāng)中,研究如何利用信任改進(jìn)推薦算法。這類研究被稱為基于信任的推薦算法(系統(tǒng))研究,并被證實(shí)能夠很好的解決傳統(tǒng)推薦系統(tǒng)所面臨的問題。基于信任的推薦是一種社會(huì)化推薦,因?yàn)檫@類推薦方法利用來自社會(huì)化網(wǎng)絡(luò)的信任信息。本文從研究如何利用信任實(shí)現(xiàn)推薦的問題出發(fā),針對(duì)具體的應(yīng)用背景,研究應(yīng)該采用怎樣的信息作為信任信息,研究如何將抽象的信任概念具體化和定量化(信任模型、信任計(jì)算方法),研究如何將定量的信任加入到推薦過程當(dāng)中。 為了利用信任提高推薦質(zhì)量,本文針對(duì)一般性的電子商務(wù)推薦問題提出了一種新的利用信任網(wǎng)絡(luò)特性的基于信任的推薦方法。信任網(wǎng)絡(luò)是由社區(qū)中的節(jié)點(diǎn)和節(jié)點(diǎn)間因信任關(guān)系連接的邊所組成的網(wǎng)絡(luò)。對(duì)信任網(wǎng)絡(luò)的結(jié)構(gòu)特性研究以及利用這些特性建立信任模型的探索還很少。首先,本文提出一種新的利用隱性信任信息構(gòu)建信任網(wǎng)絡(luò)的方法。然后,分析所建立的信任網(wǎng)絡(luò)的結(jié)構(gòu)特性,說明這個(gè)網(wǎng)絡(luò)是一個(gè)動(dòng)態(tài)的小世界網(wǎng)絡(luò)。最后,根據(jù)這些特性給出信任計(jì)算方法,提出了一個(gè)新的基于信任的推薦方法,并用實(shí)驗(yàn)證明該方法在準(zhǔn)確度上的表現(xiàn)優(yōu)于經(jīng)典的協(xié)同過濾方法。 在微博這樣一個(gè)基于用戶關(guān)系的平臺(tái)上,用戶間的交互是非常重要的。微博的用戶推薦幫助用戶找到他們可能感興趣的用戶微博。木文將上述這種基于信任的推薦方法應(yīng)用于微博用戶推薦問題當(dāng)中,提出了基于信任的微博用戶推薦方法。首先,提出一種利用微博中合適的信息構(gòu)建用戶評(píng)分的方法,并提出用戶信任的計(jì)算方法(信任模型)。分析微博中可獲得的信息,選擇那些能夠反映用戶對(duì)用戶信任或用戶對(duì)用戶感興趣和認(rèn)同的信息。利用這些信息構(gòu)建用戶對(duì)用戶的評(píng)分以及用戶對(duì)用戶的信任。然后,在此基礎(chǔ)上,給出了兩種利用信任的微博用戶推薦方法。一種是對(duì)FoF用戶推薦方法的直接改進(jìn),一種是在協(xié)同過濾的思想上利用信任進(jìn)行用戶推薦。最后,通過數(shù)值實(shí)驗(yàn),對(duì)提出的基于信任的微博用戶推薦方法的有效性和推薦質(zhì)量進(jìn)行了評(píng)價(jià)。
[Abstract]:In the past ten years, in order to solve the problems faced by traditional recommendation systems and improve the quality of recommendation, many researchers have added trust to recommendation, and studied how to use trust to improve recommendation algorithms. This kind of research is called trust based recommendation algorithm (system), and has been proved to be a good solution to the problems faced by traditional recommendation systems. Trust-based recommendation is a social recommendation because it utilizes trust information from social networks. Based on the research of how to use trust to realize recommendation, this paper studies what information should be used as trust information and how to concretize and quantify the abstract trust concept (trust model). How to add quantitative trust to the recommendation process. In order to improve the quality of recommendation by using trust, this paper proposes a new recommendation method based on trust to solve the general problem of E-commerce recommendation. A trust network is a network composed of nodes in the community and the edges connected by a trust relationship. There are few researches on the structural characteristics of trust networks and the establishment of trust models using these characteristics. First of all, this paper proposes a new method to construct trust network using implicit trust information. Then, the structural characteristics of the established trust network are analyzed, and it is shown that the network is a dynamic small-world network. Finally, according to these characteristics, a new trust based recommendation method is proposed, and the experimental results show that the proposed method is superior to the classical collaborative filtering method in accuracy. In Weibo, a user-based platform, user interaction is very important. Weibo users recommend helping users find users who may be interested in Weibo. In this paper, the trust-based recommendation method is applied to the Weibo user recommendation problem, and a trust-based Weibo user recommendation method is proposed. First of all, a new method is proposed to construct a user's score using the appropriate information in Weibo, and a trust model is proposed to calculate the user's trust. This paper analyzes the information available in Weibo, and selects the information that can reflect the user's trust in the user or the user's interest in and approval of the user. This information is used to construct the user's rating and user's trust in the user. Then, two Weibo user recommendation methods based on trust are presented. One is the direct improvement of the FoF user recommendation method, the other is the use of trust in collaborative filtering. Finally, the effectiveness and quality of the proposed Weibo user recommendation method based on trust are evaluated by numerical experiments.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.3;TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 何黎;何躍;霍葉青;;微博用戶特征分析和核心用戶挖掘[J];情報(bào)理論與實(shí)踐;2011年11期

2 平亮;宗利永;;基于社會(huì)網(wǎng)絡(luò)中心性分析的微博信息傳播研究——以Sina微博為例[J];圖書情報(bào)知識(shí);2010年06期

，

本文編號(hào)：2179053