發(fā)布時間：2018-09-09 18:18

【摘要】：近年來,互聯(lián)網(wǎng)經(jīng)歷了十分快速的發(fā)展,并且隨著各種移動智能設(shè)備的普及,以推特、微博為代表的社交網(wǎng)絡(luò)方興未艾。社交網(wǎng)絡(luò)作為網(wǎng)絡(luò)信息流動的載體,本身具有方便快捷,短小靈活的特點(diǎn),它通過“關(guān)注”和“粉絲”來連接龐大的用戶群體,通過“轉(zhuǎn)發(fā)”和“評論”來讓更多的人參與到信息的傳播過程中。社交網(wǎng)絡(luò)給人們的交流和獲取信息的方式帶來了巨大的影響,但同時它也有著自身的局限,那就是“信息過載”的現(xiàn)象,用戶在面對過于龐雜的信息時往往不能有效甄別出哪些是對自己有用的信息,這不利于信息的擴(kuò)散。為了解決這個問題就要求社交網(wǎng)絡(luò)的平臺能夠更加了解用戶,能夠?qū)τ脩舻呐d趣偏好進(jìn)行準(zhǔn)確全面的建模,從而為各種個性化服務(wù)打下堅實的基礎(chǔ)�；诖吮尘�,本文以新浪微博的用戶為研究對象,研究了多維度層次化的用戶建模方法,多維度指的是盡可能去覆蓋能夠描述用戶的特征,層次化指的是將這些特征梳理關(guān)系,形成層級結(jié)構(gòu),減輕耦合,使得模型具有可擴(kuò)展性。在論文中主要完成了以下方面的工作:1.微博爬取系統(tǒng)的設(shè)計。實現(xiàn)比較高效的爬取,處理和存儲流程;2.用戶節(jié)點(diǎn)的甄別。包括采用Page-rank算法尋找重要用戶節(jié)點(diǎn)和利用活躍度計算判斷活躍用戶節(jié)點(diǎn)兩個方面;3.對短文本的建模。為了克服短文本長度較短,用詞不規(guī)律,噪聲較多的問題,引入主題模型,訓(xùn)練帶有主題信息的段落向量,將用戶微博表示為連續(xù)值的向量;4.構(gòu)建多維度層次化模型。分別構(gòu)建模型中的各個部分,計算時對各個部分的相似度結(jié)果進(jìn)行加權(quán)求和,并將模型放在用戶好友推薦場景中進(jìn)行試驗。
[Abstract]:In recent years, the Internet has experienced a very rapid development, and with the popularity of various mobile smart devices, Twitter, Weibo as the representative of the social network is in the ascendant. As the carrier of network information flow, social network has its own characteristics of convenience, short and flexible. It connects a large number of users through "attention" and "fans". More people are involved in the dissemination of information through "retweets" and "comments". Social networks have had a great impact on the way people communicate and get information, but it also has its own limitations, that is, the phenomenon of "information overload". Users are often unable to identify which information is useful to them when they are faced with information which is too complex, which is not conducive to the spread of information. In order to solve this problem, the platform of social network is required to understand the user better, and to model the user's interest preference accurately and comprehensively, so as to lay a solid foundation for all kinds of personalized services. Based on this background, this paper takes the user of Sina Weibo as the research object, studies the multi-dimensional hierarchical user modeling method. The multi-dimension means to cover the features that can describe the user as much as possible, and the hierarchical refers to the combing of these features. The hierarchical structure is formed, and the coupling is reduced, which makes the model extensible. In this paper, I have done the following work: 1. Weibo crawled the design of the system. To achieve a more efficient crawling, processing and storage process. User node discrimination. Page-rank algorithm is used to find the important user nodes and the active user nodes are judged by the calculation of the activity degree. Modeling of short text. In order to overcome the problems of short text length, irregular use of words and more noise, a theme model is introduced to train paragraph vectors with subject information, and user Weibo is expressed as a vector with continuous values. Build a multi-dimensional hierarchical model. Each part of the model is constructed, and the similarity results of each part are calculated by weighted summation, and the model is tested in the user friend recommendation scenario.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.1;TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 黃倩;謝穎華;;一種基于網(wǎng)頁瀏覽行為的用戶興趣度計算方法[J];信息技術(shù);2015年05期

2 吳渝;馬璐璐;林茂;劉洪濤;;基于用戶影響力的意見領(lǐng)袖發(fā)現(xiàn)算法[J];小型微型計算機(jī)系統(tǒng);2015年03期

3 王玉珍;;基于Web挖掘的數(shù)字圖書館個性化服務(wù)體系研究[J];情報科學(xué);2014年04期

4 朱郭峰;楊彥;周竹榮;應(yīng)中運(yùn);韓鳳嬌;;基于領(lǐng)域的微博用戶影響力計算方法[J];西南大學(xué)學(xué)報(自然科學(xué)版);2014年03期

5 齊向華;文本信息檢索模型[J];晉圖學(xué)刊;1998年03期

，

本文編號：2233188