本文選題：數(shù)據(jù)挖掘　切入點：客戶模型　出處：《浙江理工大學(xué)》2017年碩士論文

【摘要】：網(wǎng)絡(luò)營銷模式是企業(yè)了解顧客的需求后作出企業(yè)利潤最大化的銷售策略。隨著數(shù)據(jù)量的不斷增加,個性化的銷售模式也變得日趨重要,企業(yè)應(yīng)對市場、用戶需求的不斷變化需做出更加敏捷的反映,從而增加企業(yè)與顧客直接交易的機會。個性化的推薦促使企業(yè)的營銷模式更加成熟,從而快速提高企業(yè)的銷售量和銷售金額。這種推薦模式主要采用協(xié)同過濾技術(shù)、基于內(nèi)容的推薦技術(shù)。協(xié)同過濾技術(shù)最簡單的思路就是搜索與目標對象相似興趣的鄰居用戶,并將相似用戶的偏好列表推薦給目標用戶,基于內(nèi)容的推薦算法是根據(jù)用戶之前喜歡的商品推薦相似產(chǎn)品。本文針對協(xié)同過濾技術(shù)、基于內(nèi)容的推薦算法兩種推薦技術(shù)存在的一些缺陷,考慮情景狀態(tài)下消費者的興趣指標和興趣偏好,從大量數(shù)據(jù)中獲取消費者興趣特征值,利用獲取的特征值來創(chuàng)建興趣模型,實現(xiàn)基于消費者模型的個性化智能推薦。主要工作如下:(1)針對商品的特點,基于全國零售戶銷售數(shù)據(jù),利用數(shù)據(jù)挖掘技術(shù)獲取用戶之間的關(guān)聯(lián)關(guān)系,構(gòu)建零售業(yè)的客戶價值指標。聚類算法將獲取的樣本文件進行分類,從而提取的數(shù)據(jù)可作為企業(yè)中的個性化銷售的基礎(chǔ)數(shù)據(jù)。(2)基于情景的用戶偏好的分析以及商品屬性的分類。本文采用基于情境下的建模推薦模式,從商品的自有屬性和用戶的興趣偏好的角度出發(fā),建立用戶興趣的權(quán)重值。根據(jù)權(quán)重值大小進一步分析用戶偏好的側(cè)重方向。文中情景因素主要含有位置、時間段、季節(jié)、伙伴等特定情境,用來預(yù)測用戶對商品資源的選擇行為。(3)個性化推薦算法中冷啟動問題和數(shù)據(jù)稀疏問題的研究。本文針對協(xié)同過濾算法中冷啟動的問題提出了基于用戶、商品屬性、瀏覽時間的個性化推薦技術(shù),解決個性化推薦中存在的缺陷�；谛掠脩艉糜殃P(guān)系解決數(shù)據(jù)稀疏問題。文中通過實驗數(shù)據(jù),對于企業(yè)中個性化銷售中冷啟動問題以及數(shù)據(jù)稀疏性進行了細致研究,從而有效的提高了企業(yè)中的銷售數(shù)量和銷售額度。
[Abstract]:The network marketing mode is the sales strategy that the enterprise makes the profit maximization after knowing the customer's demand.With the increasing amount of data, the individualized sales model is becoming more and more important. Enterprises should respond to the market and the changing needs of customers more quickly, thus increasing the opportunity of direct transaction between enterprises and customers.Individualized recommendation makes the marketing model more mature, thus increasing the sales volume and sales amount quickly.This recommendation mode mainly adopts collaborative filtering technology and content-based recommendation technology.The simplest idea of collaborative filtering is to search for neighbor users with similar interests to the target object, and recommend the preference list of similar users to the target users.Content-based recommendation algorithms recommend similar products according to the products users like before.In this paper, aiming at some defects of collaborative filtering technology and content-based recommendation algorithm, considering the interest index and interest preference of consumers in the situation, we obtain the characteristic value of consumer interest from a large amount of data.The interest model is created by using the obtained eigenvalues, and the personalized intelligent recommendation based on the consumer model is realized.The main work is as follows: (1) according to the characteristics of commodities, based on the national retail sales data, using data mining technology to obtain the relationship between users, build the retail customer value index.The clustering algorithm classifies the sample files so that the extracted data can be used as the basic data of personalized sales in enterprises. 2) the analysis of user preferences based on scenarios and the classification of commodity attributes.In this paper, based on the context-based modeling recommendation model, the weight of user interest is established from the point of view of the commodity's own attributes and the user's interest preference.The emphasis direction of user preference is further analyzed according to the weight value.The situational factors include location, time, season, partner and so on, which are used to predict the user's choice behavior of commodity resources. 3) the cold start problem and data sparsity problem in personalized recommendation algorithm.In order to solve the problem of cold start of collaborative filtering algorithm, this paper proposes a personalized recommendation technology based on user, commodity attributes and browsing time to solve the shortcomings of personalized recommendation.Solve the data sparse problem based on the new user's friend relationship.Based on the experimental data, this paper makes a detailed study on the cold start problem and data sparsity of individualized sales in the enterprise, which effectively improves the sales quantity and sales quota in the enterprise.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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