發(fā)布時間：2018-05-08 18:12

  本文選題：協同過濾 + Apache��； 參考：《電子科技大學》2013年碩士論文

【摘要】：隨著Internet的迅猛發(fā)展，互聯網成為了人們生活中不可或缺的一部分。人們對互聯網的需求也不僅僅限于工作，他幾乎出現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，也可以在各門戶網站，電影網站，搜索到最新的評分及影評。但隨之而來的是接入互聯網的網頁數量不斷增長。傳統的搜索算法只能呈現給所有用戶同樣的結果，無法針對不同用戶提供相應的信息，隨之產生了“信息過載”的問題。因此，個性化推薦技術應運而生。 協同過濾推薦算法是當前推薦系統中應用最廣泛的推薦算法，但是隨著電子商務的規(guī)模不斷擴大，協同過濾算法同樣遇到了一些挑戰(zhàn)，如冷啟動問題、數據稀疏性等問題。本文針對協同過濾算法進行了深入的學習和研究，并闡述了相應的組合算法和針對協同過濾算法的改進算法，取得了理想的結果。 本文的研究工作主要如下： 1、針對推薦系統和推薦算法的現狀進行了詳細了解，重點研究了協同過濾推薦算法以及Apache Mahout中關于推薦算法的相關知識；對當前的主流推薦系統和推薦算法進行了介紹，并對各種推薦算法的優(yōu)缺點進行了說明。 2、對協同過濾推薦算法進行了詳細的分析。該算法主要包括兩類:分別是基于用戶的協同過濾推薦算法（User-Based CF）和基于項目的協同過濾推薦算法（Item-Based CF），同時還重點研究和介紹了當前應用非常廣泛的Slope One推薦算法，針對這三種算法的算法原理及步驟進行了詳細的解析。 3、組合推薦算法的設計與實現。此處為本文的主要創(chuàng)新點，本文設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同過濾推薦算法和基于用戶的協同過濾推薦算法進行組合，充分利用用戶-項目評分數據集所包含的用戶和項目的相關信息來進行推薦。 4、應用Apache Mahout開源框架，使用MovieLens數據集和MAE評估標準，對傳統的基于項目的協同過濾算法、基于用戶的協同過濾算法以及Slope One算法進行了仿真實驗，對計算相似度的三種方法進行效果對比，同時對本文所述的組合推薦算法進行仿真實驗。對比了傳統的協同過濾算法以及組合算法的實驗效果，同時對實驗結果進行了分析。
[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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.3

【參考文獻】

相關期刊論文 前10條

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

2 李勇,徐振寧,張維明;Internet個性化信息服務研究綜述[J];計算機工程與應用;2002年19期

3 王國霞;劉賀平;;個性化推薦系統綜述[J];計算機工程與應用;2012年07期

4 許敏;邱玉輝;;電子商務中推薦系統存在的問題及其對策研究[J];計算機科學;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];計算機應用;2008年08期

6 吳顏;沈潔;顧天竺;陳曉紅;李慧;張舒;;協同過濾推薦系統中數據稀疏問題的解決[J];計算機應用研究;2007年06期

7 崔春生;;推薦系統中顯式評分輸入的用戶聚類方法研究[J];計算機應用研究;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推薦系統模型研究[J];情報雜志;2009年S2期

9 鄧愛林,朱揚勇,施伯樂;基于項目評分預測的協同過濾推薦算法[J];軟件學報;2003年09期

10 劉建國;周濤;汪秉宏;;個性化推薦系統的研究進展[J];自然科學進展;2009年01期

，

本文編號：1862460