本文選題：推薦引擎組合 + 個性化推薦�。� 參考：《華南理工大學》2012年碩士論文

【摘要】：近年來，隨著互聯(lián)網(wǎng)的高速發(fā)展，現(xiàn)已進入到數(shù)據(jù)過載階段，在構(gòu)建互聯(lián)網(wǎng)網(wǎng)站時需要考慮如何方便用戶在龐大的數(shù)據(jù)中獲取他所需的信息。傳統(tǒng)的互聯(lián)網(wǎng)企業(yè)的解決方式有兩種，第一種是信息目錄，如雅虎和新浪；第二種解決方式是搜索引擎，如谷歌和百度。兩者的共同點是用戶對自己的需求非常明確。但是互聯(lián)網(wǎng)用戶的現(xiàn)狀是：大部分互聯(lián)網(wǎng)用戶對需求不明確。 因此，推薦引擎應(yīng)運而生。它主動向用戶推薦用戶可能喜歡的物品，不需要用戶主動提供任何輸入，而是通過在后臺記錄并分析用戶行為數(shù)據(jù)，最后把分析結(jié)果作為個性化推薦推送給用戶。 本論文首先介紹了課題的研究背景，國內(nèi)外研究現(xiàn)狀及水平等，闡述了系統(tǒng)相關(guān)的理論基礎(chǔ)，探討了推薦系統(tǒng)相關(guān)技術(shù)與工具，，確定了系統(tǒng)的體系架構(gòu)。其次，詳細介紹了推薦系統(tǒng)的架構(gòu)、以及相關(guān)推薦系統(tǒng)的具體實現(xiàn)過程。最后利用Mahout框架把推薦引擎應(yīng)用在大規(guī)模數(shù)據(jù)環(huán)境中。 本論文的主要貢獻包括： （1）分別實現(xiàn)了三種推薦引擎，并通過組合各種推薦策略的優(yōu)點生成推薦列表。 （2）記錄并分析用戶的反饋信息，以調(diào)整各種推薦引擎組合方式；以個性化的推薦引擎組合推薦物品；實驗表明，使用了個性化引擎組合的方式產(chǎn)生的推薦結(jié)果比使用單個推薦引擎總體上獲得更好的效果。 （3）利用Hadoop的Mahout框架，把該推薦系統(tǒng)應(yīng)用到大規(guī)模數(shù)據(jù)環(huán)境中。
[Abstract]:In recent years, with the rapid development of the Internet, it has entered the stage of data overload. When constructing the Internet website, it is necessary to consider how to facilitate users to obtain the information they need in the huge data. There are two traditional solutions for Internet companies, the first is information directories, such as Yahoo and Sina, and the second is search engines, such as Google and Baidu. The common denominator between the two is that users are very clear about their needs. But the current situation of Internet users is that most Internet users are not clear about their needs. Therefore, the recommendation engine came into being. It actively recommends the items that the user may like, does not need the user to provide any input voluntarily, but records and analyzes the user behavior data in the background, finally pushes the analysis result as the personalized recommendation to the user. This paper first introduces the research background, the current situation and the level of the research at home and abroad, expounds the theoretical basis of the system, discusses the related technologies and tools of the recommendation system, and determines the architecture of the system. Secondly, the architecture of recommendation system and the implementation process of related recommendation system are introduced in detail. Finally, the recommendation engine is applied to large scale data environment using Mahout framework. The main contributions of this paper are as follows: (1) three recommendation engines are implemented, and a recommendation list is generated by combining the advantages of various recommendation strategies. (2) users' feedback information is recorded and analyzed. In order to adjust the combination of various recommendation engines; to combine the recommended items with the personalized recommendation engine; the experimental results show that, The recommendation results obtained by using the combination of personalized engines are better than that of single recommendation engines. (3) using the Mahout framework of Hadoop, the recommendation system is applied to the large-scale data environment.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP311.52

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