本文選題：數(shù)據(jù)挖掘　切入點：網(wǎng)上商城　出處：《重慶工商大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信息技術(shù)的發(fā)展和計算機網(wǎng)絡(luò)的普及,以高效、便捷、低成本為特點的電子商務(wù)得到了迅猛發(fā)展。電子商務(wù)網(wǎng)站的競爭變得很激烈,伴隨著商品數(shù)量與日俱增,以用戶為中心的時代,在用戶選購商品時,需求能否盡快得到滿足,決定著電子商務(wù)網(wǎng)站能否在競爭中立于不敗之地。網(wǎng)上商城個性化推薦變得越來越重要,基于此,本文進(jìn)行了相關(guān)研究。本論文主要關(guān)注數(shù)據(jù)挖掘中應(yīng)用聚類、決策樹和關(guān)聯(lián)規(guī)則算法在網(wǎng)上商城推薦模型中的應(yīng)用。首先介紹數(shù)據(jù)挖掘的基本理論及相關(guān)算法,針對本文采用的三種算法進(jìn)行了詳細(xì)的描述。從實際出發(fā),以銷售圖書的網(wǎng)上商城數(shù)據(jù)集為依據(jù),探討了數(shù)據(jù)挖掘中的數(shù)據(jù)集預(yù)處理過程。在數(shù)據(jù)整理完善后,使用SPSS Clementine等工具,結(jié)合聚類、關(guān)聯(lián)、決策樹等算法進(jìn)行4個個性化推薦模型的構(gòu)建。首先根據(jù)訪問習(xí)慣和關(guān)鍵詞查詢情況建立了購買行為預(yù)測模型,用于找到有價值的用戶。然后根據(jù)用戶訪問網(wǎng)站的網(wǎng)頁內(nèi)容情況,對用戶聚類細(xì)分找到最常購買的產(chǎn)品,建立產(chǎn)品推薦模型并用C5.0算法對模型進(jìn)行評估。接下來根據(jù)用戶首先訪問的3個頁面和訪問頁面之間的時間間隔對用戶聚類,并找出可能訪問的第4個頁面進(jìn)行相應(yīng)的頁面推薦。最后對用戶的基本信息特征與商品風(fēng)格之間建立關(guān)聯(lián)模型,找出用戶的特征和購買商品風(fēng)格之間的關(guān)聯(lián),對新訪客中具有相似特征的用戶進(jìn)行相應(yīng)風(fēng)格商品的推薦。針對建立的4個模型都進(jìn)行了評估與發(fā)布,進(jìn)一步展現(xiàn)推薦結(jié)果,然后描述了個性化推薦系統(tǒng)的總體設(shè)計以及在應(yīng)用過程中可能面臨的問題,最后指出論文的不足之處并提出一些改進(jìn)方法�？傮w來看,通過數(shù)據(jù)挖掘理論和電子商務(wù)實際項目結(jié)合,實現(xiàn)網(wǎng)上商城個性化推薦模型的構(gòu)建,對解決網(wǎng)上商城個性化推薦方面的問題具有重要意義。針對數(shù)據(jù)挖掘與推薦系統(tǒng),目前的研究主要集中在推薦算法的改進(jìn)方面,怎樣構(gòu)建模型以及形成個性化推薦引擎嵌入到電子商務(wù)網(wǎng)站的研究較少,本文進(jìn)行了相應(yīng)的介紹,為以后這方面的的研究提供了研究思路。
[Abstract]:With the development of information technology and the popularization of computer network, E-commerce, characterized by high efficiency, convenience and low cost, has been developed rapidly. In the era of taking the user as the center, whether or not the demand can be satisfied as soon as possible, determines whether the e-commerce website can be in an invincible position in the competition. This paper mainly focuses on the application of clustering, decision tree and association rules algorithm in online shopping mall recommendation model. Firstly, the basic theory and related algorithms of data mining are introduced. In this paper, the three algorithms used in this paper are described in detail. Based on the data set of online shopping mall which sells books, the preprocessing process of data set in data mining is discussed. Using SPSS Clementine and other tools, combining clustering, association, decision tree and other algorithms to construct four personalized recommendation models. Firstly, according to the visiting habit and keyword query, the purchase behavior prediction model is established. It is used to find valuable users. Then according to the content of the web page visited by the user, the most frequently purchased products are found by clustering the users. The product recommendation model is established and evaluated by C5.0 algorithm. Then the users are clustered according to the time interval between the three pages visited by the user first and the time interval between the access pages. Finally, the correlation model between the user's basic information characteristics and the product style is established to find out the relationship between the user's characteristics and the purchase style. For the new visitors with similar characteristics of the users of the corresponding style of goods recommended. For the establishment of the four models are evaluated and published, to further show the results of the recommendation, Then describes the overall design of the personalized recommendation system and the possible problems in the application process, finally points out the shortcomings of the paper and puts forward some improvement methods. By combining the theory of data mining with the practical project of electronic commerce, the construction of personalized recommendation model of online shopping mall is realized, which is of great significance to solve the problem of personalized recommendation of online mall. The current research mainly focuses on the improvement of recommendation algorithm, how to build a model and how to form a personalized recommendation engine embedded into e-commerce website is less, this paper gives a corresponding introduction. For the future research in this area to provide research ideas.
【學(xué)位授予單位】：重慶工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6

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本文編號：1556995