本文關(guān)鍵詞： 新聞 混合算法 推薦系統(tǒng)　出處：《云南財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：如今的因特網(wǎng)行業(yè)正在快速發(fā)展,在這個(gè)時(shí)代,信息數(shù)量巨大,更新速度飛快,使得網(wǎng)絡(luò)瀏覽者在眾多資訊中無(wú)法找到自己真正所需。為了解決這一問題,在推薦系統(tǒng)出現(xiàn)之前,人們運(yùn)用搜索引擎通過關(guān)鍵詞找到自己對(duì)信息的需求,然而某些場(chǎng)景下用戶無(wú)法很精確地明確自己所需要的關(guān)鍵詞,使得搜索引擎的效果大打折扣。個(gè)性化推薦經(jīng)過對(duì)用戶數(shù)據(jù)的分析,從而發(fā)現(xiàn)他們的相應(yīng)特征與偏好,及時(shí)提供最符合用戶的推薦結(jié)果。作為有效解決用戶沒有明確需求下的信息過載問題的工具之一,它已經(jīng)變?yōu)樵S多領(lǐng)域的研究熱點(diǎn)。個(gè)性化推薦系統(tǒng)可以智能地為因特網(wǎng)用戶推薦他們所感興趣的內(nèi)容,讓人們從海量數(shù)據(jù)的迷茫中解脫出來。在因特網(wǎng)新聞方面,個(gè)性化推薦也極其重要,今日頭條網(wǎng)(http://www.toutiao.com/)、新浪新聞網(wǎng)(http://news.sina.com.cn/)等網(wǎng)站每天都在發(fā)布各行各業(yè)的時(shí)事新聞,隨著新聞信息量與信息更新速度的不斷增大,網(wǎng)頁(yè)新聞瀏覽者難以看到自身所感興趣的新聞內(nèi)容,常常讓自己丟失在海量級(jí)別的新聞資訊中。當(dāng)遇到這一類問題時(shí),新聞推薦系統(tǒng)可以根據(jù)瀏覽者個(gè)性化的瀏覽記錄,發(fā)掘出他們的潛在瀏覽偏好,形成相應(yīng)的推薦結(jié)果。從而節(jié)約了大量瀏覽者的新聞探尋時(shí)間,提高了瀏覽者的滿意度,同時(shí)降低網(wǎng)頁(yè)新聞資源浪費(fèi)程度。利用用戶的顯式反饋信息進(jìn)行推薦的推薦方法是目前比較常見的方法,然而相對(duì)于顯式反饋,由于隱式反饋信息更容易獲取,具有普遍性,因此根據(jù)隱式反饋信息所設(shè)計(jì)的推薦系統(tǒng)具有更加廣泛的適用性,本文所設(shè)計(jì)的推薦系統(tǒng)是根據(jù)隱式反饋信息所設(shè)計(jì)的。本文主要對(duì)網(wǎng)頁(yè)新聞瀏覽者的隱式反饋數(shù)據(jù)進(jìn)行處理,對(duì)推薦模型以及推薦算法、用戶模型的構(gòu)建、推薦的混合方案和策略等內(nèi)容開展研究,將瀏覽者群體按照瀏覽頻率進(jìn)行劃分,對(duì)不同瀏覽者群體采用不同推薦算法混合,對(duì)于經(jīng)常瀏覽用戶,綜合用戶協(xié)作型過濾算法、內(nèi)容推薦算法進(jìn)行結(jié)果上的混合,對(duì)于不常瀏覽用戶,綜合了物品協(xié)作過濾算法的相似度計(jì)算以及內(nèi)容推薦算法的相似度計(jì)算法則,進(jìn)行相應(yīng)算法上的混合,并將得出的相應(yīng)推薦結(jié)果與基于隨機(jī)漫步的PersonalRank算法進(jìn)行混合。使得推薦系統(tǒng)中單一算法存在的問題如新加入物品的推薦、數(shù)據(jù)的稀疏性等不足得以降低。根據(jù)上述設(shè)計(jì)思路以及相應(yīng)算法的實(shí)現(xiàn)完成了整個(gè)新聞推薦系統(tǒng)的設(shè)計(jì),同時(shí)本文所使用的混合策略的有效性在后續(xù)實(shí)驗(yàn)中根據(jù)相應(yīng)評(píng)價(jià)指標(biāo)的對(duì)比得以驗(yàn)證。
[Abstract]:Nowadays, the Internet industry is developing rapidly. In this era, the amount of information is huge and the update speed is very fast. In order to solve this problem, the users of the Internet can't find what they really need in a lot of information. Before the emergence of recommendation systems, people use search engines to find their needs for information through keywords. However, in some scenarios, users can not identify the keywords they need very accurately. Personalized recommendation through the analysis of user data to find their corresponding characteristics and preferences. Timely provide the most consistent with the user's recommended results. As an effective solution to the problem of information overload without clear requirements of the user one of the tools. It has become a research hotspot in many fields. Personalized recommendation systems can intelligently recommend content of interest to Internet users. Personalised recommendation is also extremely important in Internet news, and today's headline is http: / / www.toutiao.com.com.com.com. (http: / / www.toutiao.com / www.toutiao.com / www.toutiao.com / www.toutiao.com / www.toutiao.com / /. Websites such as http: / / news.sina.com.cn.cn.cn.com., etc., publish news about current affairs in various industries every day, as the amount of news and the rate of update increases. Web news viewers find it difficult to see the news content they are interested in and often lose themselves in the mass of news information. When it comes to this kind of problems. The news recommendation system can discover their potential browsing preferences according to their personalized browsing records, and form the corresponding recommendation results, thus saving a large number of visitors' news search time. It can improve the satisfaction of visitors and reduce the waste of web news resources. Using explicit feedback from users to recommend is a relatively common method at present, but relative to explicit feedback. Because implicit feedback information is easier to obtain and universal, the recommendation system designed based on implicit feedback information has more extensive applicability. The recommendation system designed in this paper is based on the implicit feedback information. This paper mainly deals with the implicit feedback data of the page news viewer, and constructs the recommendation model, recommendation algorithm and user model. The content of the recommended mix scheme and strategy is studied, the viewer group is divided according to the browsing frequency, the different recommendation algorithm is used to the different visitors group, and the frequent browsing user is used. Integrated user collaborative filtering algorithm, content recommendation algorithm for the results of the hybrid, for the less frequent browsing users, the integration of articles collaborative filtering algorithm similarity calculation and content recommendation algorithm similarity calculation rules. The corresponding algorithm is mixed. The corresponding recommendation results are mixed with the PersonalRank algorithm based on random walk, which makes the problem of single algorithm in recommendation system such as the recommendation of newly added items. The lack of data sparsity can be reduced. According to the above design ideas and the corresponding algorithm to complete the design of the entire news recommendation system. At the same time, the effectiveness of the hybrid strategy used in this paper is verified by comparison of corresponding evaluation indexes in subsequent experiments.
【學(xué)位授予單位】：云南財(cái)經(jīng)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;G252

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李正兵;羅斌;翟素蘭;涂錚錚;;基于關(guān)聯(lián)圖劃分的Kmeans算法[J];計(jì)算機(jī)工程與應(yīng)用;2013年21期

2 韋素云;業(yè)寧;吉根林;張丹丹;殷曉飛;;基于項(xiàng)目類別和興趣度的協(xié)同過濾推薦算法[J];南京大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年02期

3 張培晶;宋蕾;;基于LDA的微博文本主題建模方法研究述評(píng)[J];圖書情報(bào)工作;2012年24期

4 黃創(chuàng)光;印鑒;汪靜;劉玉葆;王甲海;;不確定近鄰的協(xié)同過濾推薦算法[J];計(jì)算機(jī)學(xué)報(bào);2010年08期

5 彭德巍;胡斌;;一種基于用戶特征和時(shí)間的協(xié)同過濾算法[J];武漢理工大學(xué)學(xué)報(bào);2009年03期

6 劉建國(guó);周濤;汪秉宏;;個(gè)性化推薦系統(tǒng)的研究進(jìn)展[J];自然科學(xué)進(jìn)展;2009年01期

7 李樂;章毓晉;;非負(fù)矩陣分解算法綜述[J];電子學(xué)報(bào);2008年04期

8 張光衛(wèi);李德毅;李鵬;康建初;陳桂生;;基于云模型的協(xié)同過濾推薦算法[J];軟件學(xué)報(bào);2007年10期

9 張鋒;常會(huì)友;;使用BP神經(jīng)網(wǎng)絡(luò)緩解協(xié)同過濾推薦算法的稀疏性問題[J];計(jì)算機(jī)研究與發(fā)展;2006年04期

10 馬文峰,高鳳榮,王珊;論數(shù)字圖書館個(gè)性化信息推薦系統(tǒng)[J];現(xiàn)代圖書情報(bào)技術(shù);2003年02期

相關(guān)博士學(xué)位論文 前3條

1 古萬(wàn)榮;基于超圖模型的新聞推薦研究[D];華南理工大學(xué);2015年

2 王宏宇;商務(wù)推薦系統(tǒng)的設(shè)計(jì)研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2007年

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

相關(guān)碩士學(xué)位論文 前2條

1 孫曉會(huì);基于用戶行為的個(gè)性化新聞推薦系統(tǒng)研究[D];電子科技大學(xué);2015年

2 張海玉;基于Web使用挖掘的個(gè)性化推薦系統(tǒng)[D];西北大學(xué);2006年

，

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