WEB2.0網(wǎng)絡(luò)熱點發(fā)現(xiàn)與個性化檢索研究
發(fā)布時間：2018-11-25 11:19
【摘要】：近幾年來,所謂的Web2.0網(wǎng)站和技術(shù)發(fā)展迅速,徹底改變了互聯(lián)網(wǎng)的面貌。Web2.0網(wǎng)站強調(diào)自由創(chuàng)作和用戶參與,數(shù)以億計的網(wǎng)民在新一代的Web平臺上創(chuàng)造了海量的生動有趣的內(nèi)容。越來越豐富的互聯(lián)網(wǎng)信息資源使得用戶難以在浩如煙海的數(shù)據(jù)中找到其真正感興趣的信息,因此,各種各樣的信息檢索和搜索引擎技術(shù)得到了廣泛的關(guān)注和巨大的發(fā)展。 現(xiàn)有的Web信息檢索系統(tǒng)主要是搜索引擎,但是已有的搜索引擎還是存在著很多不足,主要表現(xiàn)為：一是Web2.0網(wǎng)站的內(nèi)容被收錄的比例很少；二是給出的結(jié)果不能反映當(dāng)前網(wǎng)絡(luò)的流行信息和熱點話題；三是檢索結(jié)果沒有針對用戶的興趣愛好來排序和篩選。針對以上幾點問題,論文所要探討的就是如何在Web2.0環(huán)境下,幫助用戶根據(jù)自己的興趣愛好從Web2.0的信息海洋里獲取流行的熱點話題。 論文主要針對Web信息檢索中的Web2.0社區(qū)網(wǎng)絡(luò)熱點發(fā)現(xiàn)以及個性化推薦進行了研究,以更好地改善用戶的檢索體驗。為了達到這個目標(biāo),論文首先提出了研究的框架,然后探討各個重要組成模塊的關(guān)鍵技術(shù),并針對Web2.0網(wǎng)站的特點提出相應(yīng)改進的算法與模型。論文的主要內(nèi)容和創(chuàng)新之處為： 1.針對Web2.0網(wǎng)站信息組織和層次結(jié)構(gòu)的特點,抽象出面向?qū)ο蟮姆植际缴疃扰老x(Object-Oriented Distributed Deep Crawler,簡稱OODDC),使用較經(jīng)濟的帶寬來與真實數(shù)據(jù)保持同步,大大提高了爬蟲的工作效率和采集數(shù)據(jù)的實時性。實驗結(jié)果也證實了面向?qū)ο蟮姆植际綄崟r深度爬蟲的優(yōu)點。 2.詳細(xì)研究了Web2.0網(wǎng)站數(shù)據(jù)格式和內(nèi)容標(biāo)簽(Tag)化的特點,在傳統(tǒng)Web信息抽取算法基礎(chǔ)上,結(jié)合向量空間模型(VSM)和實體識別算法,采用少數(shù)幾個Tag及其權(quán)重組成的向量來描述網(wǎng)頁、圖片、視頻和博客等Web對象信息本體的特征,建立了基于Tag描述的統(tǒng)一信息表示模型。 3.基于Tag描述的統(tǒng)一信息表示模型,改進了已有的話題檢測與跟蹤(TDT)算法,用快速的聚類算法檢測和聚合網(wǎng)絡(luò)話題；同時結(jié)合用戶反饋對于信息流行程度的影響,提出一種有效的網(wǎng)絡(luò)話題熱度評估算法(HotRank),對所收集的話題計算其熱度,作為排序和推薦的依據(jù)。實踐表明,以相關(guān)度和熱度共同作為檢索結(jié)果的排序依據(jù)更加吸引用戶。 4.針對現(xiàn)有用戶興趣模型的缺陷,提出一種基于主題的在線用戶興趣模型。此模型自動提取用戶訪問網(wǎng)頁的主題,并隨時根據(jù)用戶興趣的變化以非常小的代價更新。該用戶興趣模型可以運用到各種個性化服務(wù)中。實驗證明基于此模型的個性化推薦系統(tǒng)具有良好的性能。
[Abstract]:In recent years, the so-called Web2.0 website and technology have developed rapidly, completely changing the face of the Internet. Web2.0 website emphasizes free creation and user participation. Hundreds of millions of Internet users have created huge amounts of lively and interesting content on the new generation of Web platforms. More and more abundant Internet information resources make it difficult for users to find the information they are interested in the vast amount of data. Therefore, a variety of information retrieval and search engine technology has been widely concerned and greatly developed. The existing Web information retrieval system is mainly a search engine, but the existing search engine still has a lot of shortcomings, mainly as follows: first, the proportion of the content of the Web2.0 website is very small; The second is that the results can not reflect the current popular information and hot topics of the network, and the third is that the retrieval results are not sorted and filtered according to the interests and interests of the users. In view of the above problems, the thesis is to explore how to help users to get popular hot topics from the information ocean of Web2.0 according to their interests and hobbies under the Web2.0 environment. This paper mainly focuses on the hot spot discovery and personalized recommendation of Web2.0 community network in Web information retrieval in order to improve the retrieval experience of users. In order to achieve this goal, this paper first puts forward the framework of the research, then discusses the key technologies of each important component module, and puts forward the corresponding improved algorithm and model according to the characteristics of the Web2.0 website. The main contents and innovations of this paper are as follows: 1. In view of the characteristics of the information organization and hierarchy of Web2.0 Web sites, the distributed depth crawler (Object-Oriented Distributed Deep Crawler,), which is abstract to the object, uses more economical bandwidth to keep pace with the real data. The efficiency of crawler and the real time of collecting data are greatly improved. The experimental results also confirm the advantages of object-oriented distributed real-time depth reptiles. 2. The characteristics of Web2.0 website data format and content label (Tag) are studied in detail. On the basis of traditional Web information extraction algorithm, vector space model (VSM) and entity recognition algorithm are combined. A few vectors composed of Tag and their weights are used to describe the features of Web object information ontology, such as web pages, pictures, videos and blogs, and a unified information representation model based on Tag description is established. 3. Based on the unified information representation model described by Tag, the existing (TDT) algorithm of topic detection and tracking is improved, and the fast clustering algorithm is used to detect and aggregate network topics. Based on the influence of user feedback on the popularity of information, an effective heat evaluation algorithm, (HotRank), is proposed to calculate the heat of the collected topics, which can be used as the basis for sorting and recommendation. Practice shows that it is more attractive to users to use correlation and heat as the sorting basis of retrieval results. 4. Aiming at the defects of the existing user interest model, an online user interest model based on topic is proposed. This model automatically extracts the topics of users visiting web pages and updates them at a very small cost according to the changes of users' interests at any time. The user interest model can be applied to various personalized services. Experiments show that the personalized recommendation system based on this model has good performance.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3
本文編號：2355919
[Abstract]:In recent years, the so-called Web2.0 website and technology have developed rapidly, completely changing the face of the Internet. Web2.0 website emphasizes free creation and user participation. Hundreds of millions of Internet users have created huge amounts of lively and interesting content on the new generation of Web platforms. More and more abundant Internet information resources make it difficult for users to find the information they are interested in the vast amount of data. Therefore, a variety of information retrieval and search engine technology has been widely concerned and greatly developed. The existing Web information retrieval system is mainly a search engine, but the existing search engine still has a lot of shortcomings, mainly as follows: first, the proportion of the content of the Web2.0 website is very small; The second is that the results can not reflect the current popular information and hot topics of the network, and the third is that the retrieval results are not sorted and filtered according to the interests and interests of the users. In view of the above problems, the thesis is to explore how to help users to get popular hot topics from the information ocean of Web2.0 according to their interests and hobbies under the Web2.0 environment. This paper mainly focuses on the hot spot discovery and personalized recommendation of Web2.0 community network in Web information retrieval in order to improve the retrieval experience of users. In order to achieve this goal, this paper first puts forward the framework of the research, then discusses the key technologies of each important component module, and puts forward the corresponding improved algorithm and model according to the characteristics of the Web2.0 website. The main contents and innovations of this paper are as follows: 1. In view of the characteristics of the information organization and hierarchy of Web2.0 Web sites, the distributed depth crawler (Object-Oriented Distributed Deep Crawler,), which is abstract to the object, uses more economical bandwidth to keep pace with the real data. The efficiency of crawler and the real time of collecting data are greatly improved. The experimental results also confirm the advantages of object-oriented distributed real-time depth reptiles. 2. The characteristics of Web2.0 website data format and content label (Tag) are studied in detail. On the basis of traditional Web information extraction algorithm, vector space model (VSM) and entity recognition algorithm are combined. A few vectors composed of Tag and their weights are used to describe the features of Web object information ontology, such as web pages, pictures, videos and blogs, and a unified information representation model based on Tag description is established. 3. Based on the unified information representation model described by Tag, the existing (TDT) algorithm of topic detection and tracking is improved, and the fast clustering algorithm is used to detect and aggregate network topics. Based on the influence of user feedback on the popularity of information, an effective heat evaluation algorithm, (HotRank), is proposed to calculate the heat of the collected topics, which can be used as the basis for sorting and recommendation. Practice shows that it is more attractive to users to use correlation and heat as the sorting basis of retrieval results. 4. Aiming at the defects of the existing user interest model, an online user interest model based on topic is proposed. This model automatically extracts the topics of users visiting web pages and updates them at a very small cost according to the changes of users' interests at any time. The user interest model can be applied to various personalized services. Experiments show that the personalized recommendation system based on this model has good performance.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3
【引證文獻】
相關(guān)碩士學(xué)位論文 前1條
1 王星星;基于網(wǎng)絡(luò)熱點的個性化情報推薦系統(tǒng)設(shè)計與實現(xiàn)[D];華中師范大學(xué);2014年
，本文編號：2355919
本文鏈接：http://sikaile.net/kejilunwen/sousuoyinqinglunwen/2355919.html
最近更新
教材專著
