【摘要】：隨著互聯(lián)網(wǎng)的快速發(fā)展,信息呈爆炸式增長(zhǎng),用戶(hù)逐漸由信息匱乏時(shí)代邁入了信息過(guò)載時(shí)代——過(guò)量信息反而使得用戶(hù)無(wú)法找到自己需要的信息。為了方便互聯(lián)網(wǎng)用戶(hù)快速查找到所需信息,研究者提出了很多方法：門(mén)戶(hù)網(wǎng)站,相對(duì)專(zhuān)業(yè)的信息源；分類(lèi)目錄,對(duì)熱門(mén)網(wǎng)站分門(mén)別類(lèi)；搜索引擎,只需輸入關(guān)鍵詞就能找到所需的信息。但用戶(hù)需求不止于此,用戶(hù)很多時(shí)候并沒(méi)有明確信息獲取指向,個(gè)性化推薦技術(shù)以其能夠過(guò)濾大量用戶(hù)不感興趣的內(nèi)容,幫助用戶(hù)發(fā)現(xiàn)自身潛在喜歡的內(nèi)容,得到了廣泛應(yīng)用。隨著個(gè)性化推薦在電子商務(wù)領(lǐng)域大放異彩,個(gè)性化推薦技術(shù)逐步應(yīng)用到其他領(lǐng)域,比如個(gè)性化新聞推薦。互聯(lián)網(wǎng)步入到大數(shù)據(jù)時(shí)代,也給個(gè)性化新聞閱讀發(fā)展提供了良好的機(jī)遇。 新聞個(gè)性化推薦系統(tǒng)在理論研究中取得了長(zhǎng)足進(jìn)展,但仍有很多問(wèn)題亟待解決：可擴(kuò)展性問(wèn)題、時(shí)效性問(wèn)題、冷啟動(dòng)問(wèn)題、數(shù)據(jù)稀疏性問(wèn)題等,因此高效可擴(kuò)展的個(gè)性化新聞推薦系統(tǒng)是論文的研究重點(diǎn)。本文的主要工作為： 1.提出新的相似度計(jì)算方法,結(jié)合行為相似度和內(nèi)容相似度,解決了傳統(tǒng)相似度計(jì)算方法計(jì)算不準(zhǔn)確或無(wú)法計(jì)算的問(wèn)題,解決了協(xié)同過(guò)濾推薦數(shù)據(jù)稀疏性問(wèn)題。 2.提出新的適合個(gè)性化新聞推薦的可擴(kuò)展聚類(lèi)方法,更改了中心點(diǎn)選取方式和距離度量方式,使得新聞推薦系統(tǒng)的可擴(kuò)展性大大提高。 3.在個(gè)性化新聞推薦系統(tǒng)相似度計(jì)算階段和最終推薦階段融入了時(shí)間因素,保證了所推薦新聞的時(shí)效性。 4.基于MapReduce模型實(shí)現(xiàn)整個(gè)協(xié)同過(guò)濾新聞推薦系統(tǒng),使得個(gè)性化新聞推薦系統(tǒng)能夠并行運(yùn)行,可擴(kuò)展性大大提高,適應(yīng)了海量新聞和海量用戶(hù)的個(gè)性化推薦需求。 5.對(duì)聚類(lèi)方法和個(gè)性化新聞推薦方法進(jìn)行了實(shí)驗(yàn),確定了相關(guān)參數(shù),對(duì)最終基于協(xié)同過(guò)濾的個(gè)性化新聞推薦系統(tǒng)進(jìn)行了功能測(cè)試,驗(yàn)證了推薦系統(tǒng)相關(guān)功能。 論文首先分析了當(dāng)前個(gè)性化推薦技術(shù)的研究現(xiàn)狀和Hadoop云計(jì)算平臺(tái),闡述了論文提出的個(gè)性化新聞推薦的聚類(lèi)方法和基于多維相似度的個(gè)性化推薦算法,最后給出了基于MapReduce模型實(shí)現(xiàn)的新聞推薦系統(tǒng),并給出了詳細(xì)的測(cè)試和評(píng)估結(jié)果。
[Abstract]:With the rapid development of Internet and the explosive growth of information, users have gradually stepped into the era of information overload from the era of lack of information-excessive information makes it impossible for users to find the information they need. In order to facilitate Internet users to quickly find the required information, researchers have proposed many methods: portal sites, relative professional information sources, classification catalogs, classification of popular websites, and so on. Search engine, just enter keywords to find the required information. However, users need more than this, users often do not have clear information access direction, personalized recommendation technology to filter a large number of users are not interested in content, help users to find the potential content they like, has been widely used. With the development of personalized recommendation in the field of e-commerce, personalized recommendation technology is gradually applied to other fields, such as personalized news recommendation. Internet into the era of big data, but also provide a good opportunity for the development of personalized news reading. News personalized recommendation system has made great progress in theoretical research, but there are still many problems to be solved, such as scalability, timeliness, cold start, data sparsity and so on. Therefore, efficient and scalable personalized news recommendation system is the focus of this paper. The main work of this paper is as follows: 1. This paper proposes a new similarity calculation method, which combines behavior similarity with content similarity, solves the problem that the traditional similarity calculation method is inaccurate or unable to calculate, and solves the sparsity problem of collaborative filtering recommendation data. 2. A new scalable clustering method suitable for personalized news recommendation is proposed, which changes the way of selecting the center point and the way of distance measurement, which greatly improves the scalability of the news recommendation system. 3. The time factor is incorporated into the similarity calculation stage and the final recommendation stage of personalized news recommendation system, which ensures the timeliness of the recommended news. 4. Based on the MapReduce model, the whole collaborative filtering news recommendation system is implemented, which makes the personalized news recommendation system run in parallel, greatly improves the scalability, and adapts to the personalized recommendation needs of mass news and mass users. 5. The clustering method and personalized news recommendation method are experimented, and the related parameters are determined. Finally, the function test of personalized news recommendation system based on collaborative filtering is carried out, and the related functions of the recommendation system are verified. Firstly, this paper analyzes the current research status of personalized recommendation technology and Hadoop cloud computing platform, and expounds the clustering method of personalized news recommendation and the personalized recommendation algorithm based on multi-dimensional similarity. Finally, a news recommendation system based on MapReduce model is given, and the test and evaluation results are given in detail.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 張紅霞;楊淵;郎維;;基于客戶(hù)行為和興趣變化的電子商務(wù)推薦系統(tǒng)[J];寶雞文理學(xué)院學(xué)報(bào)(自然科學(xué)版);2012年02期

2 鄧愛(ài)林,朱揚(yáng)勇,施伯樂(lè);基于項(xiàng)目評(píng)分預(yù)測(cè)的協(xié)同過(guò)濾推薦算法[J];軟件學(xué)報(bào);2003年09期

相關(guān)博士學(xué)位論文 前2條

1 孫小華;協(xié)同過(guò)濾系統(tǒng)的稀疏性與冷啟動(dòng)問(wèn)題研究[D];浙江大學(xué);2005年

2 郭艷紅;推薦系統(tǒng)的協(xié)同過(guò)濾算法與應(yīng)用研究[D];大連理工大學(xué);2008年

，

本文編號(hào)：2437604