發(fā)布時間：2018-05-08 18:12

  本文選題：協(xié)同過濾 + Apache�。� 參考：《電子科技大學(xué)》2013年碩士論文

【摘要】：隨著Internet的迅猛發(fā)展，互聯(lián)網(wǎng)成為了人們生活中不可或缺的一部分。人們對互聯(lián)網(wǎng)的需求也不僅僅限于工作，他幾乎出現(xiàn)在人們生活中的每一個角落。出門吃飯，可以通過日新月異的搜索引擎，可以按照推薦率自高而低排列。要看電影，也可以在各門戶網(wǎng)站，電影網(wǎng)站，搜索到最新的評分及影評。但隨之而來的是接入互聯(lián)網(wǎng)的網(wǎng)頁數(shù)量不斷增長。傳統(tǒng)的搜索算法只能呈現(xiàn)給所有用戶同樣的結(jié)果，無法針對不同用戶提供相應(yīng)的信息，隨之產(chǎn)生了“信息過載”的問題。因此，個性化推薦技術(shù)應(yīng)運(yùn)而生。 協(xié)同過濾推薦算法是當(dāng)前推薦系統(tǒng)中應(yīng)用最廣泛的推薦算法，但是隨著電子商務(wù)的規(guī)模不斷擴(kuò)大，協(xié)同過濾算法同樣遇到了一些挑戰(zhàn)，如冷啟動問題、數(shù)據(jù)稀疏性等問題。本文針對協(xié)同過濾算法進(jìn)行了深入的學(xué)習(xí)和研究，并闡述了相應(yīng)的組合算法和針對協(xié)同過濾算法的改進(jìn)算法，取得了理想的結(jié)果。 本文的研究工作主要如下： 1、針對推薦系統(tǒng)和推薦算法的現(xiàn)狀進(jìn)行了詳細(xì)了解，重點(diǎn)研究了協(xié)同過濾推薦算法以及Apache Mahout中關(guān)于推薦算法的相關(guān)知識；對當(dāng)前的主流推薦系統(tǒng)和推薦算法進(jìn)行了介紹，并對各種推薦算法的優(yōu)缺點(diǎn)進(jìn)行了說明。 2、對協(xié)同過濾推薦算法進(jìn)行了詳細(xì)的分析。該算法主要包括兩類:分別是基于用戶的協(xié)同過濾推薦算法（User-Based CF）和基于項目的協(xié)同過濾推薦算法（Item-Based CF），同時還重點(diǎn)研究和介紹了當(dāng)前應(yīng)用非常廣泛的Slope One推薦算法，針對這三種算法的算法原理及步驟進(jìn)行了詳細(xì)的解析。 3、組合推薦算法的設(shè)計與實(shí)現(xiàn)。此處為本文的主要創(chuàng)新點(diǎn)，本文設(shè)計了一種全新的組合推薦算法，該算法主要是將基于項目的協(xié)同過濾推薦算法和基于用戶的協(xié)同過濾推薦算法進(jìn)行組合，充分利用用戶-項目評分?jǐn)?shù)據(jù)集所包含的用戶和項目的相關(guān)信息來進(jìn)行推薦。 4、應(yīng)用Apache Mahout開源框架，使用MovieLens數(shù)據(jù)集和MAE評估標(biāo)準(zhǔn)，對傳統(tǒng)的基于項目的協(xié)同過濾算法、基于用戶的協(xié)同過濾算法以及Slope One算法進(jìn)行了仿真實(shí)驗(yàn)，對計算相似度的三種方法進(jìn)行效果對比，同時對本文所述的組合推薦算法進(jìn)行仿真實(shí)驗(yàn)。對比了傳統(tǒng)的協(xié)同過濾算法以及組合算法的實(shí)驗(yàn)效果，同時對實(shí)驗(yàn)結(jié)果進(jìn)行了分析。
[Abstract]:With the rapid development of Internet, the Internet has become an indispensable part of people's lives. The need for the Internet is not limited to work, it appears in almost every corner of people's lives. Go out to eat, can be through the fast-changing search engine, can be in accordance with the recommendation rate since high and low ranking. To watch movies, you can also search for the latest ratings and reviews on various portals and movie sites. But with it, the number of web pages connected to the Internet continues to grow. The traditional search algorithm can only present the same results to all users, and can not provide the corresponding information for different users, resulting in the problem of "information overload". Therefore, personalized recommendation technology came into being. Collaborative filtering recommendation algorithm is the most widely used recommendation algorithm in current recommendation system. However, with the expansion of e-commerce, collaborative filtering algorithm also meets some challenges, such as cold start problem, data sparsity and so on. In this paper, the collaborative filtering algorithm is deeply studied and studied, and the corresponding combination algorithm and the improved algorithm for collaborative filtering algorithm are described, and the ideal results are obtained. The main work of this paper is as follows: 1. The current situation of recommendation system and recommendation algorithm is studied in detail, and the collaborative filtering recommendation algorithm and the related knowledge of recommendation algorithm in Apache Mahout are studied, and the current mainstream recommendation system and recommendation algorithm are introduced. The advantages and disadvantages of various recommended algorithms are also explained. 2. The collaborative filtering recommendation algorithm is analyzed in detail. This algorithm mainly includes two kinds: User-Based CFS, a user-based collaborative filtering recommendation algorithm, and Item-Based CFN, a project-based collaborative filtering recommendation algorithm. At the same time, it also focuses on the research and introduction of Slope One recommendation algorithm, which is widely used at present. The principle and steps of the three algorithms are analyzed in detail. 3. Design and implementation of combinatorial recommendation algorithm. For the main innovation of this paper, this paper designs a new combinatorial recommendation algorithm, which combines the project-based collaborative filtering recommendation algorithm and the user-based collaborative filtering recommendation algorithm. Make full use of the user-item scoring data set to make recommendations. 4. Apache Mahout open source framework, MovieLens dataset and MAE evaluation standard are used to simulate the traditional project-based collaborative filtering algorithm, user-based collaborative filtering algorithm and Slope One algorithm. The results of three methods for calculating similarity are compared, and the simulation experiments are carried out on the combination recommendation algorithm described in this paper. The experimental results of the traditional collaborative filtering algorithm and the combination algorithm are compared, and the experimental results are analyzed.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙亮,胡乃靜,張守志;個性化推薦算法設(shè)計[J];計算機(jī)研究與發(fā)展;2002年08期

2 李勇,徐振寧,張維明;Internet個性化信息服務(wù)研究綜述[J];計算機(jī)工程與應(yīng)用;2002年19期

3 王國霞;劉賀平;;個性化推薦系統(tǒng)綜述[J];計算機(jī)工程與應(yīng)用;2012年07期

4 許敏;邱玉輝;;電子商務(wù)中推薦系統(tǒng)存在的問題及其對策研究[J];計算機(jī)科學(xué);2001年04期

5 吳發(fā)青;賀j;夏薇薇;任磊;;一種基于用戶興趣局部相似性的推薦算法[J];計算機(jī)應(yīng)用;2008年08期

6 吳顏;沈潔;顧天竺;陳曉紅;李慧;張舒;;協(xié)同過濾推薦系統(tǒng)中數(shù)據(jù)稀疏問題的解決[J];計算機(jī)應(yīng)用研究;2007年06期

7 崔春生;;推薦系統(tǒng)中顯式評分輸入的用戶聚類方法研究[J];計算機(jī)應(yīng)用研究;2011年08期

8 聶規(guī)劃;章志龍;王銳;;基于語義詞典的電子商務(wù)推薦系統(tǒng)模型研究[J];情報雜志;2009年S2期

9 鄧愛林,朱揚(yáng)勇,施伯樂;基于項目評分預(yù)測的協(xié)同過濾推薦算法[J];軟件學(xué)報;2003年09期

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

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