【摘要】：當前，互聯(lián)網(wǎng)時代的信息傳遞已經(jīng)深刻地改變了人們的信息共享方式，Web已經(jīng)成為人們獲取信息的主要途徑。搜索引擎的出現(xiàn)從一定程度上滿足了人們信息檢索的需求，但它并不能滿足不同領(lǐng)域，不同層次用戶的需求。個性化推薦技術(shù)應(yīng)信息檢索的需求而生，它是個性化服務(wù)的一種模式，本質(zhì)是信息過濾。 個性化推薦系統(tǒng)不僅能在社會經(jīng)濟中發(fā)揮巨大的價值，同時也是個非常值得研究的科學問題。目前最為經(jīng)典的推薦方法是協(xié)同過濾推薦，而比較新穎的推薦方法則是基于網(wǎng)絡(luò)結(jié)構(gòu)的推薦。一般來說，推薦方法都是在單一資源網(wǎng)絡(luò)中研究用戶興趣，并未過多涉及到多種資源組合成的多維網(wǎng)絡(luò)，多維網(wǎng)絡(luò)中的個性化推薦是一個比較新穎的研究角度。 針對上述問題，本文在協(xié)同過濾和基于網(wǎng)絡(luò)結(jié)構(gòu)的推薦思想啟發(fā)下，在研究社會網(wǎng)絡(luò)和復(fù)雜網(wǎng)絡(luò)理論的基礎(chǔ)上，將多維網(wǎng)絡(luò)和復(fù)雜網(wǎng)絡(luò)的分析方法引入個性化推薦的研究中來，提出一種動態(tài)多維社會網(wǎng)絡(luò)的個性化推薦方法。首先提出多維交疊網(wǎng)絡(luò)及其映射網(wǎng)絡(luò)的定義，構(gòu)建用戶之間多維加權(quán)網(wǎng)絡(luò)模型；在此基礎(chǔ)上，引入局域世界演化理論，生成符合本文環(huán)境的網(wǎng)絡(luò)模型演化規(guī)則，生成動態(tài)多維網(wǎng)絡(luò)模型；使用識別重疊網(wǎng)絡(luò)簇的復(fù)雜網(wǎng)絡(luò)聚類算法CPM尋找鄰居用戶，并最終做出推薦。 本文的主要工作和創(chuàng)新點包括： 1.通過分析社會網(wǎng)絡(luò)的概念和特點，著重研究多模網(wǎng)絡(luò)的定義和用戶在多模網(wǎng)絡(luò)中的活動規(guī)律，，給出了一個比較清晰的多維交疊網(wǎng)絡(luò)及其映射網(wǎng)絡(luò)的數(shù)學化定義。盡管多維網(wǎng)絡(luò)的概念早有學者提出，定義也是多種多樣，但目前還沒有一個統(tǒng)一的數(shù)學化定義。文章通過對多維網(wǎng)絡(luò)的形成和多維網(wǎng)絡(luò)轉(zhuǎn)化成一維網(wǎng)絡(luò)的方法進行研究，歸納總結(jié)現(xiàn)有的多維網(wǎng)絡(luò)形成和降維方法，給出一個并非普適的，但能比較清晰地刻畫多維交疊網(wǎng)絡(luò)及其映射網(wǎng)絡(luò)形成過程的定義。通過構(gòu)建用戶之間多維加權(quán)網(wǎng)絡(luò)模型的方式來描述參與個性化推薦的用戶，改進了原有的只使用興趣描述文件的用戶建模方法。 2.在建立的用戶之間的多維加權(quán)網(wǎng)絡(luò)中，分析其具有的復(fù)雜網(wǎng)絡(luò)特性，尤其是局域演化規(guī)則。根據(jù)經(jīng)典的局域世界演化理論，以用戶之間相似度為節(jié)點連接概率因素，改進連接概率公式，提出符合本文用于個性化推薦的多維加權(quán)網(wǎng)絡(luò)的局域世界演化理論模型，并以此生成動態(tài)多維網(wǎng)絡(luò)。動態(tài)多維網(wǎng)絡(luò)模型是進行個性化推薦算法的前提條件，是對用戶數(shù)據(jù)的挖掘和更新模型。 3.使用能識別重疊網(wǎng)絡(luò)簇的CPM算法進行用戶聚類。本文建立的動態(tài)多維網(wǎng)絡(luò)模型具有復(fù)雜網(wǎng)絡(luò)的特征；同時，由于用戶興趣的廣泛性和多維交疊網(wǎng)絡(luò)的特點，在尋找鄰居用戶群時極有可能發(fā)生聚類的重疊。因此，采用能識別重疊聚類簇結(jié)構(gòu)的復(fù)雜網(wǎng)絡(luò)聚類算法尋找鄰居用戶，符合本文個性化推薦的網(wǎng)絡(luò)環(huán)境。此外，本文還使用了基于用戶相似性的最近鄰查找方法，并給出了推薦策略。 4.在生成的動態(tài)多維網(wǎng)絡(luò)中進行個性化推薦算法的仿真實驗，從不同角度驗證了所提算法的有效性。在與常用推薦算法的比較，動態(tài)因素的考量以及聚類方法的選擇三方面給出了驗證結(jié)果，并通過不同的評價標準驗證了算法的優(yōu)勢性并給出了算法的推薦系統(tǒng)應(yīng)用模型。
[Abstract]:At present, the information transmission of Internet era has changed people's information sharing mode deeply, and Web has become the main way of people getting information. The appearance of search engine meets the needs of people's information retrieval to a certain extent, but it does not meet the needs of users in different fields and different levels. Personalized recommendation technology should be generated by information retrieval. It is a mode of personalized service. It is the essence of information filtering. Personalized recommendation system can not only play a great value in the social economy, but also a scientific question worth studying at the same time At present, the most classic recommended method is collaborative filtering recommendation, while the newer recommended method is based on the push of the network structure Recommendation. Generally, the recommended method is to study user's interests in a single resource network, not too many multi-dimensional networks combining multiple resources, and the personalized recommendation in multi-dimensional network is a relatively new research angle Aiming at the above problems, based on the research of social network and complex network theory, this paper introduces the method of multi-dimensional network and complex network into the research of personalized recommendation based on the research of social network and complex network theory. In the research, a dynamic multi-dimensional social network personalized push is proposed The method comprises the following steps: firstly, a multidimensional overlapping network and a definition of a mapping network of a multidimensional overlapping network are proposed, a multidimensional weighted network model among users is constructed; based on the method, a local world evolution theory is introduced to generate network model evolution rules which are consistent with the environment, and a dynamic multidimensional network is generated. A complex network clustering algorithm CPM identifying overlapping network clusters is used for finding neighbor users and finally Recommended. The main work of this article and The innovation points include: 1. By analyzing the concept and characteristics of the social network, the definition of the multi-mode network and the rule of activity of the users in the multi-mode network are studied, a relatively clear multi-dimensional overlapping network and its mapping network are given. Although the concept of multi-dimensional network has been put forward by scholars, the definition is also varied, but there is still no system at present This paper studies the formation of multi-dimensional networks and the transformation of multi-dimensional networks into one-dimensional networks, sums up the existing multi-dimensional network forming and dimensionality reduction methods, and gives a non-generalized, but can clearly depict multidimensional overlapping networks and their mapping networks. A user who participates in the personalized recommendation is described by constructing a multi-dimensional weighted network model between users, the original use interest description file is improved, in a multi-dimensional weighted network between established users, analyzing the complex network characteristics it has, in particular to a local evolution rule. According to the classical local world evolution theory, the probability factor is connected with the similarity between the users as the node connection probability factor, the connection probability formula is improved, the local-area world evolution theory model for the multi-dimensional weighted network for the personalized recommendation is proposed and the dynamic multi-dimensional network model is a precondition of carrying out the personalized recommendation algorithm, mining and updating models according to. 3. Use the ability to identify overlapping network clusters The dynamic multi-dimensional network model established in this paper has the characteristics of complex networks, and at the same time, due to the wide range of interests of users and the characteristics of multi-dimensional overlapping networks, Therefore, it is possible to find neighbor users by using complex network clustering algorithms which can identify overlapping cluster structures and conform to the present invention. In addition, a recent neighbor search based on user similarity is also used in this paper In this paper, a simulation experiment of personalized recommendation algorithm is given in the generated dynamic multi-dimensional network. The effectiveness of the proposed algorithm is verified. In comparison with the commonly used recommendation algorithms, the dynamic factors are considered as well as the three aspects of the method of clustering, and the advantages of the algorithm are verified by different evaluation criteria.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP391.3

【引證文獻】

相關(guān)期刊論文 前1條

1 郭平;劉波;沈岳;;農(nóng)業(yè)云大數(shù)據(jù)自組織推送關(guān)鍵技術(shù)綜述[J];軟件;2013年03期

本文編號：2289425