本文選題：推薦系統(tǒng) + 聚類算法�。� 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：近幾年來的時(shí)代最熱點(diǎn)無疑是移動(dòng)互聯(lián)網(wǎng)時(shí)代的到來,互聯(lián)網(wǎng)化的浪潮席卷了現(xiàn)代社會(huì)的各個(gè)領(lǐng)域。隨著移動(dòng)通訊工具的普及以及互聯(lián)網(wǎng)信息技術(shù)的發(fā)展,“信息過載”的現(xiàn)象已經(jīng)成為無法避免的焦點(diǎn)問題。“信息過載”的挑戰(zhàn)是人人無法回避的,無論是對(duì)于信息的發(fā)布者,還是對(duì)于信息的接收者,都必須去面對(duì)這個(gè)問題。對(duì)于信息的接受者尤甚,如何快速地獲得自身需要的,有價(jià)值的信息值得關(guān)注。由于以上原因,個(gè)性信息推薦成為了計(jì)算機(jī)領(lǐng)域的一個(gè)研究熱點(diǎn)。校園信息化是社會(huì)信息化的一個(gè)重要領(lǐng)域,信息過載和信息獲取效率的問題同樣突出。當(dāng)前校園內(nèi)學(xué)生獲取校園內(nèi)新聞通知的方式極為多樣化,卻缺乏效率,缺少精準(zhǔn)性和智能性。本文圍繞以上背景,關(guān)注校園信息化建設(shè),以及怎樣做一個(gè)校園新聞推薦系統(tǒng)做詳細(xì)的分析與研究。本文的研究?jī)?nèi)容有如下:文章通過對(duì)校園推薦系統(tǒng)的研究現(xiàn)狀與發(fā)展進(jìn)行分析,以此提出了未來的個(gè)性化推薦發(fā)展方向?qū)⑹墙Y(jié)合各個(gè)推薦方法優(yōu)點(diǎn)的組合推薦方法。對(duì)現(xiàn)有的個(gè)性化推薦方法如基于關(guān)聯(lián)規(guī)則推薦,基于內(nèi)容推薦,協(xié)同過濾推薦等進(jìn)行了研究,分析其中的技術(shù)原理,應(yīng)用領(lǐng)域和各自優(yōu)缺點(diǎn)。結(jié)合對(duì)現(xiàn)有推薦方法的研究分析,本文提出了一種先基于文本聚類,再將內(nèi)容推薦和協(xié)同過濾結(jié)合的推薦方法。因此本文采用K-means算法對(duì)新聞利用關(guān)鍵詞特征進(jìn)行新聞文本聚類處理;在關(guān)注文本內(nèi)容相似性的同時(shí),結(jié)合對(duì)用戶行為的協(xié)同過濾分析進(jìn)行推薦。基于本文提出的推薦方案,設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)基于Android平臺(tái)的校園信息推薦系統(tǒng),應(yīng)用于聚類及推薦技術(shù),新聞采集的實(shí)現(xiàn),服務(wù)器的搭建,數(shù)據(jù)庫及Android客戶端的實(shí)現(xiàn)并給出了測(cè)試與驗(yàn)證。
[Abstract]:In recent years, the most hot spot of the times is undoubtedly the arrival of the mobile Internet era, the wave of Internet swept through all fields of modern society. With the popularity of mobile communication tools and the development of Internet information technology, the phenomenon of "information overload" has become an unavoidable focus. The challenge of "information overload" is unavoidable, both for the publisher and receiver of information. Especially for the recipients of information, how to quickly obtain their own needs, valuable information is worth paying attention to. Because of the above reasons, personality information recommendation has become a research hotspot in the field of computer. Campus informatization is an important field of social informatization. The problems of information overload and information acquisition efficiency are also prominent. At present, students in campus get news notification in a variety of ways, but lack of efficiency, accuracy and intelligence. This paper focuses on the construction of campus information, and how to do a campus news recommendation system for detailed analysis and research. The research contents of this paper are as follows: through the analysis of the current situation and development of campus recommendation system, this paper proposes that the future development direction of personalized recommendation will be combined with the advantages of each recommendation method. The existing personalized recommendation methods, such as association rule based recommendation, content based recommendation and collaborative filtering recommendation, are studied, and their technical principles, application fields and their respective advantages and disadvantages are analyzed. Based on the research and analysis of the existing recommendation methods, this paper proposes a recommendation method based on text clustering, and then combines content recommendation with collaborative filtering. In this paper, K-means algorithm is used to deal with the news text clustering using keyword features, while paying attention to the similarity of text content, combining with the collaborative filtering analysis of user behavior to recommend. Based on the recommendation scheme proposed in this paper, a campus information recommendation system based on Android platform is designed and implemented, which is used in clustering and recommendation technology, news collection and server construction. The implementation of database and Android client is given.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 翟東海;魚江;高飛;于磊;丁鋒;;最大距離法選取初始簇中心的K-means文本聚類算法的研究[J];計(jì)算機(jī)應(yīng)用研究;2014年03期

2 馬獲蕾;湯海鳳;;Android系統(tǒng)中SQLite數(shù)據(jù)庫的研究[J];電腦知識(shí)與技術(shù);2013年28期

3 王念橋;;應(yīng)用MVP模式改進(jìn)軟件架構(gòu)[J];計(jì)算機(jī)時(shí)代;2012年04期

4 胡軍偉;秦奕青;張偉;;正則表達(dá)式在Web信息抽取中的應(yīng)用[J];北京信息科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年06期

5 龍樹全;趙正文;唐華;;中文分詞算法概述[J];電腦知識(shí)與技術(shù);2009年10期

6 丁振國;張卓;黎靖;;基于Hash結(jié)構(gòu)的逆向最大匹配分詞算法的改進(jìn)[J];計(jì)算機(jī)工程與設(shè)計(jì);2008年12期

7 馮少榮;肖文俊;;基于語義距離的高效文本聚類算法[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年05期

8 方蔚濤;楊丹;李珩;胡月;汪顯偉;;數(shù)字化校園信息門戶的設(shè)計(jì)研究[J];計(jì)算機(jī)科學(xué);2007年03期

相關(guān)碩士學(xué)位論文 前3條

1 李佳珊;個(gè)性化新聞推薦引擎中新聞分組聚類技術(shù)的研究與實(shí)現(xiàn)[D];北京郵電大學(xué);2013年

2 虞璐;Web2.0時(shí)代豆瓣網(wǎng)的網(wǎng)絡(luò)文化研究[D];南京師范大學(xué);2012年

3 周昭濤;文本聚類分析效果評(píng)價(jià)及文本表示研究[D];中國科學(xué)院研究生院（計(jì)算技術(shù)研究所）;2005年

，

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