本文選題：定向廣告 + 搜索引擎��； 參考：《北京郵電大學(xué)》2012年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)廣告業(yè)的快速發(fā)展,定向廣告作為一種新興的網(wǎng)絡(luò)廣告模式也隨之迅速發(fā)展,這種廣告模式以其精準(zhǔn)、及時(shí)、高效的特點(diǎn)備受人們的關(guān)注。定向廣告是一種投放在網(wǎng)頁(yè)上、與網(wǎng)頁(yè)內(nèi)容或者用戶(hù)自身的行為特征相關(guān)的廣告投放模式,按定向模式的不同可以分為內(nèi)容定向廣告和行為定向廣告。與傳統(tǒng)平面媒體、電視媒體相對(duì)而言,互聯(lián)網(wǎng)的豐富交互手段使得互聯(lián)網(wǎng)廣告具備了傳統(tǒng)媒體無(wú)法比擬的廣告投放優(yōu)化空間。PC終端豐富的多媒體交互能力、狀態(tài)保持能力為廣告定向投放行為提供了良好的平臺(tái)。 定向廣告的基礎(chǔ)是垂搜索引擎。垂直搜索引擎,是針對(duì)某一個(gè)行業(yè)的專(zhuān)業(yè)搜索引擎,是搜索引擎的細(xì)分和延伸,它針對(duì)某一特定領(lǐng)域、某一特定人群或某一特定需求提供、有一定價(jià)值的信息和相關(guān)服務(wù)。而廣告垂直搜索引擎則是針對(duì)競(jìng)價(jià)廣告行業(yè)的專(zhuān)業(yè)搜索引擎,它以結(jié)構(gòu)化廣告信息為搜索內(nèi)容,向查詢(xún)商品的用戶(hù)提供相關(guān)廣告的信息。垂直搜索引擎與通用網(wǎng)頁(yè)搜索引擎有很大的不同,它和普通的網(wǎng)頁(yè)搜索引擎的最大區(qū)別是對(duì)被查信息進(jìn)行了結(jié)構(gòu)化信息抽取,也就是將網(wǎng)頁(yè)的非結(jié)構(gòu)化數(shù)據(jù)變成特定的結(jié)構(gòu)化信息數(shù)據(jù),網(wǎng)貞搜索是以網(wǎng)頁(yè)為最小單位,而垂直搜索是以結(jié)構(gòu)化數(shù)據(jù)為最小單位,然后將這些數(shù)據(jù)存儲(chǔ)到數(shù)據(jù)庫(kù),進(jìn)行進(jìn)一步的加工處理,如：去重、分類(lèi)等,最后分詞、索引再以搜索的方式滿(mǎn)足用戶(hù)的需求。 廣告垂直搜索引擎通要而對(duì)海量數(shù)據(jù),引擎庫(kù)中通常會(huì)存儲(chǔ)幾千萬(wàn)乃至上億條的廣告信息數(shù)據(jù)。在索引量和搜索量大到一定程度的時(shí)候,索引查詢(xún)、更新的效率會(huì)逐漸降低,服務(wù)器的壓力逐漸升高,整個(gè)搜索引擎的利用率可以說(shuō)是越來(lái)越低了,并且隨著海量數(shù)據(jù)存儲(chǔ)帶來(lái)的困難,設(shè)計(jì)一個(gè)良好的分布式垂直搜索引擎將成為一個(gè)垂直搜索引擎能否面向未來(lái)發(fā)展的關(guān)鍵因素。 如何向用戶(hù)推送最相關(guān)的廣告,以及如何解決每天數(shù)以?xún)|計(jì)的廣告投放的問(wèn)題是本文研究的重點(diǎn)。本文采用了文本內(nèi)容匹配、長(zhǎng)期用戶(hù)行為分析、短期用戶(hù)行為分析、關(guān)聯(lián)推薦、融合模式等多種方法來(lái)為用戶(hù)推送其最喜愛(ài)的廣告,并通過(guò)多節(jié)點(diǎn)分布式計(jì)算體系,解決了超大規(guī)模并發(fā)下的實(shí)時(shí)請(qǐng)求處理問(wèn)題。利用多層次數(shù)據(jù)處理架構(gòu)解決了海量數(shù)據(jù)的匯總統(tǒng)計(jì)需求,實(shí)現(xiàn)出了一種在數(shù)據(jù)量、訪(fǎng)問(wèn)量增加的情況下可擴(kuò)展的一種服務(wù)架構(gòu)。
[Abstract]:With the rapid development of Internet advertising industry, targeted advertising as a new network advertising model has also developed rapidly. This advertising model has attracted people's attention for its accurate, timely and efficient characteristics. Targeted advertising is a kind of advertising mode which is placed on the web page and related to the content of the web page or the behavior characteristics of the user itself. It can be divided into content oriented advertising and behavioral targeted advertising according to the different orientation mode. Compared with the traditional print media and TV media, the rich interactive means of the Internet make the Internet advertisement have the multimedia interaction ability which the traditional media can not compare with the optimization space of advertising placement. PC terminal is rich in multimedia interaction. State-keeping ability provides a good platform for advertising targeting. The basis of targeted advertising is the vertical search engine. Vertical search engine is a professional search engine for a certain industry. It is the subdivision and extension of a search engine. It aims at a specific field, a specific population or a specific need to provide information and related services of a certain value. The vertical advertising search engine is a professional search engine for the bidding advertising industry. It uses structured advertising information as the search content to provide relevant advertising information to the users who query the products. The vertical search engine is very different from the general web search engine. The biggest difference between vertical search engine and common web search engine is the structured information extraction. In other words, the unstructured data of a web page is changed into a specific structured information data. Web search takes the web page as the smallest unit, while the vertical search takes the structured data as the minimum unit, and then stores the data into the database. Further processing, such as removing heavy, classifying, finally partitioning, indexing and searching to meet the needs of users. Advertising vertical search engines usually store tens of millions and even hundreds of millions of ad information data for mass data. When the number of indexes and searches is large to a certain extent, the efficiency of index queries and updates will gradually decrease, the pressure on the server will gradually increase, and the utilization rate of the whole search engine can be said to be getting lower and lower. With the difficulties of mass data storage, the design of a good distributed vertical search engine will be a key factor for the future development of a vertical search engine. How to push the most relevant ads to users and how to solve the problem of hundreds of millions of ads every day is the focus of this paper. In this paper, text content matching, long term user behavior analysis, short term user behavior analysis, association recommendation, fusion mode and other methods are used to push their favorite advertisements for users, and multi-node distributed computing system is adopted. It solves the problem of real-time request processing under super large scale concurrency. The multilevel data processing architecture is used to solve the statistical requirement of mass data, and an extensible service architecture is implemented with the increase of data volume and traffic.
【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】：2012
【分類(lèi)號(hào)】：TP311.52

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 相春雷;;2009年中國(guó)搜索引擎市場(chǎng)趨勢(shì)分析[J];軟件世界;2010年02期

2 ;揭秘搜索引擎收錄網(wǎng)站的秘密[J];計(jì)算機(jī)與網(wǎng)絡(luò);2010年Z1期

3 劉治綱;葉水生;;基于多本體的搜索引擎框架設(shè)計(jì)[J];南昌航空大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年02期

4 金凡;顧進(jìn)廣;;一種改進(jìn)的T-Spider分布式爬蟲(chóng)[J];微電子學(xué)與計(jì)算機(jī);2011年08期

5 馬s，

本文編號(hào)：2111682