本文選題：推薦系統(tǒng) + 組合引擎　； 參考：《華南理工大學(xué)》2013年碩士論文

【摘要】：進(jìn)入二十一世紀(jì)之后，人類互聯(lián)網(wǎng)的大數(shù)據(jù)時(shí)代，我們面臨著一個(gè)嚴(yán)重的問題就是信息過載。在互聯(lián)網(wǎng)時(shí)代有許多探索解決信息過載的方法，信息分類網(wǎng)站和搜索引擎就已經(jīng)在解決信息過載問題上取得了成功。通過信息分類來(lái)解決信息過載的網(wǎng)站有雅虎和新浪，而谷歌和百度則是通過搜索來(lái)解決信息過載的。推薦系統(tǒng)被認(rèn)為是一種更加優(yōu)秀的解決方法，相比前兩者，，推薦系統(tǒng)更加智能和主動(dòng)。面對(duì)著整個(gè)用互聯(lián)網(wǎng)的時(shí)候用戶許多時(shí)候是不知道自己的需求是什么，而信息分類和搜索引擎是建立在用戶通過關(guān)鍵字或者信息所屬類目去查找的。 推薦引擎是主動(dòng)發(fā)送推薦的信息給用戶。它運(yùn)用集體智慧來(lái)幫助用戶對(duì)海量信息作出選擇。集體智慧是是一種共享的或者群體的智能，以及集結(jié)眾人的意見進(jìn)而轉(zhuǎn)化為決策的一種過程，許多個(gè)體通過合作和競(jìng)爭(zhēng)所顯現(xiàn)出來(lái)的智慧。推薦引擎依托海量數(shù)據(jù)，分析用戶的行為、特征以及愛好，并為用戶找出符合其興趣的物品。 本論文先闡述研究背景、國(guó)內(nèi)外相關(guān)研究，并深入研究了推薦系統(tǒng)的發(fā)展，推薦算法及其應(yīng)用，同時(shí)還探討了大數(shù)據(jù)處理框架Hadoop的原理。本文通過對(duì)推薦系統(tǒng)理論的研究和應(yīng)用以及對(duì)Hadoop的研究，確定了推薦系統(tǒng)的架構(gòu)，并詳細(xì)設(shè)計(jì)了推薦系統(tǒng)，同時(shí)還闡述了推薦系統(tǒng)的主要部分的實(shí)現(xiàn)。 本文的主要貢獻(xiàn)有以下幾點(diǎn)： 1）設(shè)計(jì)了一個(gè)水平擴(kuò)展推薦算法的推薦系統(tǒng)框架，可以動(dòng)態(tài)添加和修改推薦引擎，并根據(jù)主流的協(xié)同重點(diǎn)分析和設(shè)計(jì)了基于協(xié)同過濾的引擎。 2）使用基于用戶動(dòng)態(tài)反饋的權(quán)值計(jì)算方法來(lái)綜合各個(gè)推薦結(jié)果，從而組成一個(gè)推薦引擎組合，提高了整個(gè)推薦系統(tǒng)的測(cè)評(píng)指標(biāo)。 3）使用Hadoop大數(shù)據(jù)平臺(tái)實(shí)現(xiàn)推薦系統(tǒng)來(lái)應(yīng)對(duì)推薦系統(tǒng)海量數(shù)據(jù)的計(jì)算，從而提升了計(jì)算效率，減少了系統(tǒng)的反應(yīng)時(shí)間。
[Abstract]:After entering the 21 century, the big data era of human Internet, we face a serious problem is information overload. In the Internet era, there are many ways to solve the problem of information overload. Information classification websites and search engines have been successful in solving the problem of information overload. Websites that use information classification to resolve information overload include Yahoo and Sina, while Google and Baidu use search to resolve information overload. Recommendation system is considered to be a better solution, compared with the first two, the recommendation system is more intelligent and active. In the face of the entire use of the Internet, users often do not know what their own needs, and information classification and search engines are built on the user through the keyword or information to the category to find. Recommendation engine is the initiative to send the recommended information to the user. It uses collective wisdom to help users make choices about massive amounts of information. Collective wisdom is a kind of shared or collective intelligence, and a process that gathers the opinions of others and transforms them into decision making. Many individuals are shown to be wise through cooperation and competition. Based on massive data, the recommendation engine analyzes the user's behavior, characteristics and hobbies, and finds out the objects that fit the user's interests. This paper first describes the research background, domestic and foreign related research, and deeply studies the development of recommendation system, recommendation algorithm and its application. At the same time, it also discusses the principle of big data processing framework Hadoop. Through the research and application of recommendation system theory and the research of Hadoop, this paper determines the framework of recommendation system, designs the recommendation system in detail, and expounds the realization of the main part of recommendation system. The main contributions of this paper are as follows: 1) A recommendation system framework of horizontal extended recommendation algorithm is designed, which can dynamically add and modify the recommendation engine, and analyze and design the engine based on collaborative filtering according to the main collaborative emphasis. 2) the weight calculation method based on user dynamic feedback is used to synthesize each recommendation result, thus a recommendation engine combination is formed, and the evaluation index of the whole recommendation system is improved. 3) the Hadoop big data platform is used to realize the recommendation system to deal with the computation of the massive data of the recommendation system, which improves the computing efficiency and reduces the reaction time of the system.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.3

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