本文選題：網(wǎng)絡購買行為　切入點：預測　出處：《北京交通大學》2017年碩士論文

【摘要】：網(wǎng)絡購物已成為人們日常生活中必不可缺的一部分。網(wǎng)絡購物中顧客和商家不需要面對面交易,這使得商家不能很好地把握消費者的想法和需求。但是顧客的購物行為的任何一個細節(jié)卻服務器記錄著,這使得通過分析這些行為數(shù)據(jù)來了解消費者的偏好甚至實現(xiàn)預測其購買行為成為可能。因此本文提出了使用大數(shù)據(jù)分析方法——機器學習算法從大量的消費者歷史網(wǎng)購行為數(shù)據(jù)中學習出隱含在其中的購買模式獲得模型,當新的顧客購物行為數(shù)據(jù)被輸入到該模型中時,即可實現(xiàn)對顧客購買行為的預測。本文首先對網(wǎng)絡購買行為的影響因素和預測研究進行了文獻綜述,深入了解網(wǎng)絡購買行為的本質并發(fā)現(xiàn)目前基于大數(shù)據(jù)分析的網(wǎng)絡購買行為研究仍處于起步階段。所以本文以阿里巴巴舉辦的大數(shù)據(jù)競賽作為研究背景,并將用戶在阿里巴巴電子商務平臺上真實的購物行為數(shù)據(jù)作為研究數(shù)據(jù),通過使用機器學習算法對網(wǎng)絡購買行為進行建模。首先使用Sql Server在原始數(shù)據(jù)的基礎上構造了 322個特征,并基于Extra-trees算法提取出對于預測購買行為最有幫助的10大特征。然后本文選擇了兩種常用的機器學習算法:邏輯斯特回歸和支持向量機,將這10個特征分別輸入兩個算法得到兩個預測模型。最后本文基于Soft-voting的方法對以上兩個算法進行融合。實驗證明,融合后的模型較單一的模型具有更好的預測效果。本文的研究以數(shù)據(jù)為驅動,旨在實證說明使用消費者的歷史購物行為預測其未來購買行為的可行性。本文的預測模型可以被用于購物網(wǎng)站的推薦系統(tǒng)中,實現(xiàn)用戶界面的完全個性化,激發(fā)顧客的購買欲望,提高電子商務平臺的轉化率。
[Abstract]:Online shopping has become an essential part in people's daily life. The online shopping customers and businesses do not need face-to-face transactions, the business is not very good grasp of consumer's ideas and needs. But any details of a customer's shopping behavior is a record of the server, through the analysis of these data to understand consumer behavior the prediction of their buying behavior preference even possible. Therefore this paper proposes the use of large data analysis methods, machine learning algorithms from the consumer behavior of online shopping history of a large number of data obtained in the model of implicit learning mode of purchasing them, when the customer shopping behavior data are input to the model, can realize the prediction of customer purchase behavior. Firstly, influence on online purchasing behavior factors and prediction research conducted a literature review, in-depth understanding of the network The essence of purchase behavior and found that the current analysis of large data network based on purchasing behavior research is still in its initial stage. So the Alibaba held a big data race as the research background, and the shopping user behavior data in the Alibaba real e-commerce platform as the research data, the learning algorithm to model the network purchasing behavior using the machine. The first to use Sql Server 322 features are constructed based on the original data, and Extra-trees algorithm to extract the features of 10 help for the prediction based on the purchase behavior. Then this paper chooses two kinds of machine learning algorithms: logistic regression and support vector machine, and the 10 features are input to the two algorithm two model. Finally fusion of the above two algorithms based on the Soft-voting method. The experimental results show that after fusion The prediction of the model is single model has better. This study based on data driven, empirical to illustrate the history of consumer shopping behavior to predict the feasibility of future purchase behavior. The prediction model can be used to recommend shopping site, is completely personalized user interface now, stimulate the customers desire to buy, to improve the conversion rate of e-commerce platform.

【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F713.55

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本文編號：1728719