【摘要】：隨著互聯(lián)網(wǎng)的快速普及和智能攜帶產(chǎn)品的推廣,越來越多的人投入到互聯(lián)網(wǎng)中,互聯(lián)網(wǎng)成為現(xiàn)代廣告業(yè)務(wù)的新載體�；ヂ�(lián)網(wǎng)廣告投放具有效益高、覆蓋面廣等特點,因此受到前所未有的關(guān)注。傳統(tǒng)的互聯(lián)網(wǎng)廣告投放充滿著隨機性和不確定性,導(dǎo)致用戶在上網(wǎng)的時候面臨著鋪天蓋地的廣告,降低了用戶的上網(wǎng)體驗,造成了廣告位的轉(zhuǎn)化率低等問題。為了提升用戶體驗,研究者提出了個性化廣告推薦技術(shù),并成為了近年來的研究熱點。個性化廣告推薦技術(shù)的核心是用戶模型,個性化廣告服務(wù)的質(zhì)量與用戶模型的精確性有直接相關(guān)性。本文提出了基于顯隱式信息結(jié)合的用戶模型的個性化廣告推薦技術(shù)。開展了如下研究工作:(1)提出一種改進的隱式建模方法。傳統(tǒng)的隱式建模方法將用戶所有的日志信息作為建模信息。改進的隱式建模方法對用戶上網(wǎng)日志進行分析,將查詢詞與歷史文檔進行相似性對比,過濾掉相似性值較小的文檔,提高用戶興趣模型精確度。(2)提出了顯隱式信息相結(jié)合的用戶建模技術(shù)。顯隱式信息結(jié)合的用戶建模首先是根據(jù)用戶提交的用戶信息初始化用戶模型,然后通過對用戶上網(wǎng)歷史信息進行分析,構(gòu)建隱式用戶模型,對初始用戶模型進行更新。(3)提出了一種基于用戶模型的協(xié)同過濾廣告推薦算法。協(xié)同過濾技術(shù)是根據(jù)用戶-項目評分矩陣,挖掘出用戶相似集合,通過近鄰集合中興趣相近的用戶給目標(biāo)用戶推薦信息。本文將上面提出的用戶興趣模型應(yīng)用到協(xié)同過濾算法中,利用用戶模型矩陣替代評分矩陣,實驗結(jié)果表明基于用戶模型的協(xié)同過濾推薦算法能夠提升廣告推薦的精確度。
[Abstract]:With the rapid popularization of the Internet and the promotion of intelligent carrier products, more and more people put into the Internet, the Internet has become a new carrier of modern advertising business. Internet advertising has the characteristics of high efficiency and wide coverage, so it has received unprecedented attention. The traditional Internet advertising is full of randomness and uncertainty, which results in the users facing numerous advertisements when they surf the Internet, which reduces the users' experience on the Internet and causes the low conversion rate of advertisements. In order to improve the user experience, the researchers put forward the personalized advertising recommendation technology, which has become a research hotspot in recent years. The core of personalized advertising recommendation technology is user model. The quality of personalized advertising service is directly related to the accuracy of user model. In this paper, a personalized advertising recommendation technology based on explicit and implicit information combination is proposed. The main works are as follows: (1) an improved implicit modeling method is proposed. The traditional implicit modeling method takes all user log information as modeling information. The improved implicit modeling method analyzes the user log, compares the query words with the historical documents, and filters out the documents with small similarity value. Improve the accuracy of user interest model. (2) A user modeling technique combining explicit and implicit information is proposed. Firstly, the user model is initialized according to the user information submitted by the user, and then the implicit user model is constructed by analyzing the historical information of the user. The initial user model is updated. (3) A collaborative filtering advertising recommendation algorithm based on user model is proposed. Collaborative filtering technology is based on the user-item scoring matrix to mine the similar set of users and recommend information to the target users through the users with similar interests in the nearest neighbor set. In this paper, the user interest model is applied to the collaborative filtering algorithm, and the user model matrix is used to replace the scoring matrix. The experimental results show that the collaborative filtering recommendation algorithm based on the user model can improve the accuracy of advertising recommendation.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

【參考文獻】

相關(guān)期刊論文 前5條

1 宮玲玲;喬鴻;;移動信息服務(wù)中用戶興趣建模研究[J];網(wǎng)絡(luò)安全技術(shù)與應(yīng)用;2012年12期

2 許海玲;吳瀟;李曉東;閻保平;;互聯(lián)網(wǎng)推薦系統(tǒng)比較研究[J];軟件學(xué)報;2009年02期

3 林霜梅;汪更生;陳弈秋;;個性化推薦系統(tǒng)中的用戶建模及特征選擇[J];計算機工程;2007年17期

4 趙銀春,付關(guān)友,朱征宇;基于Web瀏覽內(nèi)容和行為相結(jié)合的用戶興趣挖掘[J];計算機工程;2005年12期

5 李振星,陸大玨,任繼成,唐衛(wèi)清,唐榮錫;基于潛在語義索引的Web信息預(yù)測采集過濾方法[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2004年01期

相關(guān)博士學(xué)位論文 前1條

1 羅莉娟;基于網(wǎng)絡(luò)生活方式的綜合價值個性化推薦機制研究[D];北京郵電大學(xué);2015年

相關(guān)碩士學(xué)位論文 前5條

1 李曉笛;Web文本挖掘技術(shù)研究及應(yīng)用[D];北京交通大學(xué);2015年

2 葛譯澤;互聯(lián)網(wǎng)廣告精準(zhǔn)投放平臺設(shè)計與實現(xiàn)[D];成都理工大學(xué);2015年

3 馬曉迪;基于網(wǎng)絡(luò)結(jié)構(gòu)的個性化推薦系統(tǒng)的研究與開發(fā)[D];浙江工業(yè)大學(xué);2014年

4 吳志媛;基于潛在語義索引的Web文本挖掘[D];江南大學(xué);2013年

5 李勇;智能檢索中基于本體的個性化用戶建模技術(shù)及應(yīng)用[D];國防科學(xué)技術(shù)大學(xué);2002年

，

本文編號：2263210