本文選題：智能電子商務(wù) + 數(shù)據(jù)挖掘��； 參考：《天津大學(xué)》2010年博士論文

【摘要】：隨著Internet技術(shù)的不斷發(fā)展,電子商務(wù)系統(tǒng)給商家和客戶帶來(lái)了越來(lái)越多的信息,于是各種基于電子商務(wù)的個(gè)性化服務(wù)應(yīng)運(yùn)而生,個(gè)性化服務(wù)成為一個(gè)研究的熱點(diǎn),引起人們的廣泛關(guān)注。從客戶角度出發(fā),客戶更關(guān)心順利找到自己需要的商品。電子商務(wù)系統(tǒng)可以模擬商店銷售人員向用戶提供商品推薦,幫助用戶找到所需商品,從而順利完成購(gòu)買過(guò)程。從企業(yè)角度出發(fā),發(fā)現(xiàn)高價(jià)值的商品組合,幫助企業(yè)優(yōu)化客戶,為企業(yè)創(chuàng)造更多的利潤(rùn),是企業(yè)實(shí)施電子商務(wù)系統(tǒng)的最終目的。本文從這兩個(gè)角度出發(fā),對(duì)個(gè)性化服務(wù)的電子商務(wù)模型相關(guān)問(wèn)題進(jìn)行了深入研究。在文章開(kāi)頭介紹了數(shù)據(jù)挖掘的主要方法和研究熱點(diǎn)。評(píng)價(jià)了相關(guān)問(wèn)題的研究進(jìn)展,簡(jiǎn)要介紹了進(jìn)化計(jì)算理論基礎(chǔ)、整體框架以及最新研究進(jìn)展。以下是本文主要研究?jī)?nèi)容和創(chuàng)新性工作,主要包括: (1)利用系統(tǒng)分析理論和價(jià)值鏈理論,提出了基于個(gè)性化服務(wù)的智能化電子商務(wù)模式,并分析這種商務(wù)模式框架特點(diǎn)和優(yōu)勢(shì)。然后,從企業(yè)戰(zhàn)略角度分析了該模式在電子商務(wù)市場(chǎng)環(huán)境下競(jìng)爭(zhēng)優(yōu)勢(shì),并與傳統(tǒng)模式進(jìn)行了比較分析。最后,結(jié)合典型案例實(shí)證分析了這種新型的電子商務(wù)模式的現(xiàn)實(shí)意義,針對(duì)這種電子商務(wù)模式特點(diǎn)制定一套競(jìng)爭(zhēng)戰(zhàn)略,并對(duì)戰(zhàn)略規(guī)劃和實(shí)施進(jìn)行詳細(xì)論述。 (2)提出了一種基于遺傳算法的顧客購(gòu)買行為特征提取算法。該算法分為兩個(gè)階段,第一階段,采用Tanimoto相似度來(lái)度量顧客間購(gòu)買行為,并設(shè)計(jì)遺傳聚類算法對(duì)顧客群體進(jìn)行劃分,把具有相似購(gòu)買行為顧客聚集為一類。然后,針對(duì)不同顧客群體的購(gòu)買行為特征,設(shè)計(jì)一種基于遺傳算法的多種群特征提取方法,從各個(gè)子群體中發(fā)現(xiàn)顧客的購(gòu)買行為的知識(shí)。為了增強(qiáng)種群內(nèi)部協(xié)同進(jìn)化能力和規(guī)則質(zhì)量,我們采用最近鄰替代遺傳策略(q-NNR)和局部搜索策略。我們使用實(shí)際零售數(shù)據(jù)集對(duì)整個(gè)算法進(jìn)行了驗(yàn)證,并與經(jīng)典的Apriori算法進(jìn)行比較,實(shí)驗(yàn)結(jié)果表明該算法在不需要產(chǎn)生頻繁項(xiàng)集的情況下,可以比較高效生成精簡(jiǎn)規(guī)則集,在規(guī)則形式方面也更加靈活。最后,我們對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行了詳細(xì)的分析。 (3)利用關(guān)聯(lián)分析模型,建立一個(gè)多目標(biāo)優(yōu)化模型。該模型把商品直接收益和由于交叉銷售因素產(chǎn)生的間接利潤(rùn)作為兩個(gè)獨(dú)立的優(yōu)化目標(biāo),并設(shè)計(jì)多目標(biāo)遺傳算法進(jìn)行求解。為了增加種群多樣性和提高算法搜索能力,加入個(gè)體修補(bǔ)、填充策略和局部搜索策略。最后,用實(shí)際零售數(shù)據(jù)集對(duì)該多目標(biāo)優(yōu)化模型和多目標(biāo)遺傳算法進(jìn)行了驗(yàn)證。通過(guò)實(shí)驗(yàn)分析表明,這種多目標(biāo)優(yōu)化算法可以獲得豐富信息,為決策者制定具有針對(duì)性營(yíng)銷策略提供比較全面的信息。
[Abstract]:With the development of Internet technology, e-commerce system brings more and more information to merchants and customers. Draw people's wide attention. From the point of view of customers, customers are more concerned about finding the goods they need. E-commerce system can simulate the store salesperson to provide the product recommendation to the user, help the user to find the needed goods, and thus complete the purchase process smoothly. From the point of view of enterprise, it is the ultimate goal of the enterprise to realize the electronic commerce system to find the high value commodity combination, to help the enterprise optimize the customer, and to create more profit for the enterprise. From these two angles, this paper makes a deep research on the e-commerce model of personalized service. At the beginning of the article, the main methods and research focus of data mining are introduced. The research progress of the related problems is evaluated, and the theoretical basis, the global framework and the latest research progress of evolutionary computing are briefly introduced. The following are the main contents and innovative work of this paper, including: 1) based on the system analysis theory and value chain theory, the intelligent e-commerce model based on personalized service is proposed, and the characteristics and advantages of this business model framework are analyzed. Then, this paper analyzes the competitive advantage of this model in the electronic commerce market environment from the angle of enterprise strategy, and compares it with the traditional model. Finally, this paper analyzes the practical significance of this new mode of electronic commerce based on typical cases, formulates a set of competitive strategy according to the characteristics of this mode of electronic commerce, and discusses in detail the planning and implementation of the strategy. A genetic algorithm based on genetic algorithm (GA) is proposed to extract the feature of customer purchase behavior. The algorithm is divided into two stages. In the first stage, Tanimoto similarity is used to measure the purchase behavior between customers, and genetic clustering algorithm is designed to divide the customer population into a class of customers with similar purchase behavior. Then, a multi-population feature extraction method based on genetic algorithm is designed to find the knowledge of the customer's purchase behavior from each sub-population according to the purchase behavior characteristics of different customer groups. In order to enhance the ability of coevolution and the quality of rules within the population, we adopt the nearest neighbor alternative genetic strategy (Q-NNR) and the local search strategy. We use the real retail data set to verify the algorithm and compare it with the classical Apriori algorithm. The experimental results show that the algorithm can efficiently generate the reduced rule set without generating frequent itemsets. There is also greater flexibility in the form of rules. Finally, we analyze the experimental results in detail. A multi-objective optimization model is established by using the correlation analysis model. The model takes the direct profit of commodities and the indirect profit caused by cross-selling as two independent optimization objectives and designs a multi-objective genetic algorithm to solve the problem. In order to increase population diversity and improve the search ability of the algorithm, individual patching, filling strategy and local search strategy are added. Finally, the multi-objective optimization model and multi-objective genetic algorithm are verified with real retail data sets. The experimental results show that the multi-objective optimization algorithm can obtain abundant information and provide more comprehensive information for decision-makers to formulate targeted marketing strategies.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2010
【分類號(hào)】：F274;F713.36;F224

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相關(guān)期刊論文 前8條

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本文編號(hào)：1930195