面向電商平臺(tái)客戶持續(xù)購買問題的情境化推薦模型研究
發(fā)布時(shí)間：2018-07-29 05:51
【摘要】：隨著移動(dòng)商務(wù)、情境感知、物聯(lián)網(wǎng)的發(fā)展,電子商務(wù)的疆界被大大拓展,我們已經(jīng)步入一個(gè)商務(wù)信息“大數(shù)據(jù)”時(shí)代。然而,電子商務(wù)中海量、無序的業(yè)務(wù)信息與客戶需求之間的矛盾也日益凸顯。在這種環(huán)境下,一方面電商平臺(tái)獲取新客戶的成本急劇增加,另一方面電子商務(wù)企業(yè)想要維持和提高收益率,保留現(xiàn)有客戶和提升客戶持續(xù)性購買意愿變得非常迫切。以B2C為應(yīng)用核心的電商平臺(tái)積累了海量的數(shù)據(jù),但是客戶面臨“信息豐富、但有用信息獲取困難”的難題。如何根據(jù)客戶的喜好、歷史網(wǎng)絡(luò)行為以及其他客戶群體興趣等信息,主動(dòng)為客戶提供符合其偏好的商品,提供個(gè)性化的信息推薦服務(wù),從而激發(fā)客戶持續(xù)性地網(wǎng)上瀏覽、購買等行為是電子商務(wù)平臺(tái)面臨的巨大挑戰(zhàn)。個(gè)性化推薦方法作為客戶在海量商務(wù)信息中獲取偏好商品信息的重要手段,近年來受到了廣泛的關(guān)注。但是,電商平臺(tái)的客戶興趣具有復(fù)雜性,且購買行為受到情境影響后變得更加地不確定性與跳躍性�，F(xiàn)有的個(gè)性化推薦服務(wù)未能很好的應(yīng)對上述問題,導(dǎo)致客戶不斷流失。電商平臺(tái)迫切需要準(zhǔn)確高效地提供既符合客戶內(nèi)外情境,又滿足客戶偏好的信息服務(wù),即提供情境化推薦來支持客戶的持續(xù)性購買行為。為此,本論文“面向電商平臺(tái)客戶持續(xù)購買問題的情境化推薦模型”,研究對象為B2C平臺(tái)的客戶,研究范疇是客戶的持續(xù)購買問題,以多維情境影響下的個(gè)性化推薦方法為手段,在分析電商客戶情境的多樣化、個(gè)性化以及動(dòng)態(tài)變化等特征的基礎(chǔ)上,以網(wǎng)絡(luò)消費(fèi)者決策行為理論、分布式認(rèn)知理論與馬斯洛需求層次理論等作為個(gè)性化推薦方法的理論基礎(chǔ),綜合聚類、決策樹、關(guān)聯(lián)規(guī)則、馬爾科夫、協(xié)同過濾、本體建模等方法研究電商平臺(tái)客戶持續(xù)購買問題的解決路徑,并應(yīng)用于電商平臺(tái)不同階段對客戶持續(xù)購買的推薦服務(wù)中。主要研究工作如下：1.面向電商平臺(tái)客戶持續(xù)購買問題的情境化推薦模型研究電商平臺(tái)客戶持續(xù)購買問題針對的是已經(jīng)在平臺(tái)上購買過商品的老客戶,他們的興趣變化及購物行為可以歸為兩種情況：第一種客戶的興趣在一定時(shí)期內(nèi)是穩(wěn)定的,針對這一類型客戶,本文提出構(gòu)建分布式與差異化情境影響下的客戶興趣模型,然后利用情境化推薦方法完成商品的推送；第二種客戶的興趣由于電商多維度情境的變化產(chǎn)生了漂移(分為漸進(jìn)式與突變式),針對這類型客戶,本文建立動(dòng)態(tài)興趣模型并持續(xù)監(jiān)測來適應(yīng)客戶興趣的變化,利用自適應(yīng)的情境化推薦方法完成商品的推送。本研究創(chuàng)新性的提出涵蓋上述電子商務(wù)平臺(tái)客戶不同興趣特征的情境化推薦模型,多維度分析電子商務(wù)中的情境與客戶興趣特征,建立一個(gè)融合情境、客戶興趣的個(gè)性化推薦知識(shí)模型,作為情境化推薦應(yīng)用的知識(shí)支撐。2.基于客戶敏感情境的個(gè)性化推薦方法研究針對電商平臺(tái)興趣未發(fā)生漂移的客戶持續(xù)購買問題。傳統(tǒng)推薦模型未能很好地考慮不同情境類型對客戶需求的分布式和差異化影響,以及情境化推薦服務(wù)自適應(yīng)性差等不足,提出了基于客戶敏感情境的個(gè)性化推薦方法。該方法分析各種敏感情境類型及其具體實(shí)例對客戶興趣的差異化影響,設(shè)計(jì)一種基于分布式認(rèn)知理論的客戶興趣提取算法;然后,結(jié)合分布式影響因子,提取出基于敏感情境認(rèn)知的多維度情境客戶興趣。在上述客戶興趣提取的基礎(chǔ)上,將提取出的敏感情境引入到協(xié)同過濾推薦過程,計(jì)算情境化客戶興趣之間的相似度,并設(shè)計(jì)一種融入情境相似度的改進(jìn)協(xié)同過濾推薦算法。3.考慮客戶興趣漸進(jìn)式漂移特征的情境化推薦方法研究針對電商平臺(tái)興趣漸進(jìn)式漂移的客戶持續(xù)購買問題,首先提出了基于改進(jìn)型FP-Tree的關(guān)聯(lián)規(guī)則算法,有效的提升了電子商務(wù)環(huán)境下客戶興趣規(guī)則模式挖掘的效率；其次,定義了客戶的情境強(qiáng)度和情境關(guān)聯(lián)度,并對其進(jìn)行了量化處理。在此基礎(chǔ)上,提出了融入情境貢獻(xiàn)度的客戶興趣挖掘及漂移偵測算法,完成對情境貢獻(xiàn)度影響下客戶興趣的建模與表達(dá),并利用關(guān)聯(lián)規(guī)則置信度與支持度的變化來對情境化客戶偏好模式進(jìn)行漂移偵測；最終,改進(jìn)基于項(xiàng)目的協(xié)同過濾推薦算法,采用關(guān)聯(lián)規(guī)則中項(xiàng)目的關(guān)系尋找候選項(xiàng)目集,且提出了將影響客戶興趣的情境貢獻(xiàn)度代替評分以應(yīng)對數(shù)據(jù)的稀疏性,提高了計(jì)算項(xiàng)目間相似度的準(zhǔn)確性。4.考慮客戶興趣突變式漂移特征的情境化推薦方法研究針對電商平臺(tái)興趣突變式漂移的客戶持續(xù)購買問題�？紤]個(gè)性化推薦服務(wù)中存在難以有效適應(yīng)外部情境與用戶認(rèn)知等心理因素變化帶來的興趣顯著進(jìn)化問題,提出了一個(gè)新的情境化推薦方法。首先,行為動(dòng)機(jī)經(jīng)典理論——“馬斯洛需求層次理論”表明人的需求是會(huì)發(fā)生變化的,從而解釋了人的興趣會(huì)出現(xiàn)漂移原因。根據(jù)該原理設(shè)計(jì)了客戶商品或者類別偏好、購買行為與客戶需求層次的對應(yīng)機(jī)制；然后,利用上述機(jī)制提出了基于本體與隱馬爾科夫的客戶興趣層次判定算法,對客戶興趣進(jìn)行表達(dá)與建模；其次,引入客戶活躍度概念并提出融入情境的客戶活躍度計(jì)算方法來解決推薦服務(wù)中的冷啟動(dòng)與稀疏性問題；最終,提出融入客戶活躍度的動(dòng)態(tài)協(xié)同過濾推薦算法,持續(xù)監(jiān)測、學(xué)習(xí)客戶興趣變化規(guī)律,通過選擇性擴(kuò)充候選推薦內(nèi)容,以及判定跳躍式興趣趨勢來主動(dòng)適應(yīng)突變式漂移問題。5.面向電商平臺(tái)客戶持續(xù)購買問題的情境化推薦應(yīng)用研究將本研究提出的模型與方法應(yīng)用于電子商務(wù)情境下客戶持續(xù)購買問題中,設(shè)計(jì)推薦系統(tǒng)的體系框架并展開具體應(yīng)用,通過實(shí)際數(shù)據(jù)來驗(yàn)證與分析本研究方法在某B2C電商平臺(tái)客戶持續(xù)購買問題中的推薦效果。最后,給出了若干提高電子商務(wù)個(gè)性化推薦質(zhì)量及客戶持續(xù)購買意愿的措施和建議,為電子商務(wù)企業(yè)的個(gè)性化推薦應(yīng)用研究,以及客戶保持提供了參考。
[Abstract]:With the development of mobile commerce, situational awareness, and the development of the Internet of things, the boundaries of e-commerce have been greatly expanded. We have entered a "big data" era of business information. However, the contradiction between the mass of the electronic commerce, the disordered business information and the customer needs has become increasingly prominent. In this environment, on the one hand, the e-commerce platform can obtain new customers. On the other hand, e-commerce enterprises want to maintain and improve the rate of return. It is very urgent to retain the existing customers and improve the customer's willingness to purchase continuously. With the B2C as the core of the e-commerce platform, it has accumulated a lot of data, but the customer is faced with the problem of "rich information, but difficult to obtain information." According to customer preferences, historical network behavior and other customer group interest information, it is a great challenge to stimulate customer's continuous online browsing and purchase by providing customers with their preferred goods and providing personalized information recommendation services. In recent years, the important means of obtaining the preference for commodity information in mass business information has attracted wide attention. However, the customer interest of the e-commerce platform is complex, and the purchase behavior has become more uncertain and hopping after the situation is affected. The existing personalized recommendation service has not been able to deal with the above problems well, leading to the customers. The e-commerce platform urgently needs to provide accurate and efficient information services that meet both the customer's internal and external situation and satisfy the customer's preference, that is, providing situational recommendation to support the customer's continuous purchase behavior. Therefore, this paper "the situational recommendation model for the continuous purchase of questions for e-commerce platform customers", the research object is B2C On the basis of analyzing the diversification, individuation and dynamic changes of e-commerce customers, the research category is the customer's continuous purchase problem and the personalized recommendation method under the influence of multi-dimensional situation. On the basis of the characteristics of the diversification, individuation and dynamic change of e-commerce customer situation, the theory of network consumer decision behavior, distributed cognition theory and Maslow's requirement hierarchy theory, etc. As the theoretical basis of personalized recommendation method, comprehensive clustering, decision tree, association rules, Markoff, collaborative filtering, ontology modeling, and other methods to study the solution path of customer continuous purchase problem of e-commerce platform customers, and applied to the recommendation service of customer continuous purchase at different stages of the e-commerce platform. The main research work is as follows: 1. power facing electricity. A situational recommendation model for the continuous purchase of business platform customers research on the continuous purchase of e-commerce platform customers is aimed at the old customers who have purchased goods on the platform. Their interest changes and shopping behavior can be classified into two situations: the first customer's interest is stable for a certain period of time, for this type of customer. In this paper, we propose a customer interest model under the influence of distributed and differentiated situations, and then use a situational recommendation method to complete the push of goods. The interest of the second customers is drifting due to the changes in the multi-dimensional context of e-commerce (divided into gradual and mutational type). In this paper, the dynamic interest model is established and held for this type of customer. Continuous monitoring to adapt to the change of customer interest, use the adaptive situational recommendation method to complete the push of goods. This research innovatively proposes a situational recommendation model which covers the different interest characteristics of the customers in the e-business platform, analyzes the situation and customer interest characteristics in the e-commerce, and establishes a fusion situation and the customer. The personalized recommendation knowledge model of interest, as the knowledge support of the situational recommendation application,.2. based on the personalized recommendation method based on the customer sensitive situation. The traditional recommendation model fails to consider the distributed and differentiation of different situation types to customer needs. In this way, a personalized recommendation method based on the sensitive situation of customers is proposed. This method analyzes the difference between the sensitive situation types and their specific instances, and designs a customer interest extraction algorithm based on the distributed cognitive theory. On the basis of the above customer interest extraction, the extracted sensitive situation is introduced into the collaborative filtering recommendation process, and the similarity between the situational customer interests is calculated, and an improved collaborative filtering recommendation algorithm.3. for situational similarity is designed, and an improved collaborative filtering algorithm is designed. The situational recommendation method, which concerns the progressive drift characteristics of customer interest, aims at the customer's continuous purchase problem for the gradual drift of the interest of e-commerce platform. First, a association rule algorithm based on improved FP-Tree is proposed, which effectively improves the efficiency of customer interest rule mining under the e-commerce environment. Secondly, the customer is defined. The situation intensity and context correlation degree are quantified. On this basis, the customer interest mining and drift detection algorithm which integrates the situation contribution degree is proposed, and the model and expression of customer interest are modeled and expressed under the influence of the situation contribution, and the situational customer preference pattern is used by the change of the confidence and support degree of the association rules. In the end, the collaborative filtering recommendation algorithm based on the project is improved, the relationship between the items in the association rules is used to find the candidate item set, and the situation contribution degree which affects the customer's interest is replaced by the score to deal with the sparsity of the data, and the accuracy of the similarity between the calculated items is improved by.4., which considers the mutation of the customer's interest. The situational recommendation method of the drift feature studies the problem of customer continuous purchase for the abrupt drift of the interest of the e-commerce platform. Considering the significant evolution of the interests that are difficult to adapt to the changes of the psychological factors such as the external situation and the user cognition in the personalized recommendation service, a new situational recommendation method is proposed. The "Maslow demand hierarchy theory" shows that human needs will change, which explains the cause of people's interest drift. According to this principle, we designed the customer's commodity or category preference, the corresponding mechanism of the purchase behavior and the customer demand level; then, the mechanism based on the above mechanism is put forward based on this mechanism. The customer interest level determination algorithm of body and hidden Markov is expressed and modeled for customer interest. Secondly, the concept of customer activity is introduced and the customer activity calculation method is put forward to solve the cold start and sparsity problems in the recommendation service. Finally, a dynamic collaborative filtering recommendation which is integrated into the customer activity is proposed. Algorithm, continuous monitoring, learning of changing rules of customer interest, selective extension of candidate recommendations, and determination of jumping interest trends to adapt to abrupt drift problem.5., a situational recommendation Application Research on e-commerce platform customers' continuous purchase problem, the model and method proposed in this study are applied to e-commerce situation. In the problem of customer continuing purchase, the system framework of the recommendation system is designed and the specific application is carried out. Through the actual data, the recommendation effect of this research method in the continuous purchase problem of a B2C e-commerce platform customer is verified and analyzed. Finally, some measures to improve the personalized recommendation quality of electronic commerce and the continuous purchase intention of customers are given. And suggestions for e-commerce enterprises personalized recommendation application research and customer retention provides a reference.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：F724.6
[Abstract]:With the development of mobile commerce, situational awareness, and the development of the Internet of things, the boundaries of e-commerce have been greatly expanded. We have entered a "big data" era of business information. However, the contradiction between the mass of the electronic commerce, the disordered business information and the customer needs has become increasingly prominent. In this environment, on the one hand, the e-commerce platform can obtain new customers. On the other hand, e-commerce enterprises want to maintain and improve the rate of return. It is very urgent to retain the existing customers and improve the customer's willingness to purchase continuously. With the B2C as the core of the e-commerce platform, it has accumulated a lot of data, but the customer is faced with the problem of "rich information, but difficult to obtain information." According to customer preferences, historical network behavior and other customer group interest information, it is a great challenge to stimulate customer's continuous online browsing and purchase by providing customers with their preferred goods and providing personalized information recommendation services. In recent years, the important means of obtaining the preference for commodity information in mass business information has attracted wide attention. However, the customer interest of the e-commerce platform is complex, and the purchase behavior has become more uncertain and hopping after the situation is affected. The existing personalized recommendation service has not been able to deal with the above problems well, leading to the customers. The e-commerce platform urgently needs to provide accurate and efficient information services that meet both the customer's internal and external situation and satisfy the customer's preference, that is, providing situational recommendation to support the customer's continuous purchase behavior. Therefore, this paper "the situational recommendation model for the continuous purchase of questions for e-commerce platform customers", the research object is B2C On the basis of analyzing the diversification, individuation and dynamic changes of e-commerce customers, the research category is the customer's continuous purchase problem and the personalized recommendation method under the influence of multi-dimensional situation. On the basis of the characteristics of the diversification, individuation and dynamic change of e-commerce customer situation, the theory of network consumer decision behavior, distributed cognition theory and Maslow's requirement hierarchy theory, etc. As the theoretical basis of personalized recommendation method, comprehensive clustering, decision tree, association rules, Markoff, collaborative filtering, ontology modeling, and other methods to study the solution path of customer continuous purchase problem of e-commerce platform customers, and applied to the recommendation service of customer continuous purchase at different stages of the e-commerce platform. The main research work is as follows: 1. power facing electricity. A situational recommendation model for the continuous purchase of business platform customers research on the continuous purchase of e-commerce platform customers is aimed at the old customers who have purchased goods on the platform. Their interest changes and shopping behavior can be classified into two situations: the first customer's interest is stable for a certain period of time, for this type of customer. In this paper, we propose a customer interest model under the influence of distributed and differentiated situations, and then use a situational recommendation method to complete the push of goods. The interest of the second customers is drifting due to the changes in the multi-dimensional context of e-commerce (divided into gradual and mutational type). In this paper, the dynamic interest model is established and held for this type of customer. Continuous monitoring to adapt to the change of customer interest, use the adaptive situational recommendation method to complete the push of goods. This research innovatively proposes a situational recommendation model which covers the different interest characteristics of the customers in the e-business platform, analyzes the situation and customer interest characteristics in the e-commerce, and establishes a fusion situation and the customer. The personalized recommendation knowledge model of interest, as the knowledge support of the situational recommendation application,.2. based on the personalized recommendation method based on the customer sensitive situation. The traditional recommendation model fails to consider the distributed and differentiation of different situation types to customer needs. In this way, a personalized recommendation method based on the sensitive situation of customers is proposed. This method analyzes the difference between the sensitive situation types and their specific instances, and designs a customer interest extraction algorithm based on the distributed cognitive theory. On the basis of the above customer interest extraction, the extracted sensitive situation is introduced into the collaborative filtering recommendation process, and the similarity between the situational customer interests is calculated, and an improved collaborative filtering recommendation algorithm.3. for situational similarity is designed, and an improved collaborative filtering algorithm is designed. The situational recommendation method, which concerns the progressive drift characteristics of customer interest, aims at the customer's continuous purchase problem for the gradual drift of the interest of e-commerce platform. First, a association rule algorithm based on improved FP-Tree is proposed, which effectively improves the efficiency of customer interest rule mining under the e-commerce environment. Secondly, the customer is defined. The situation intensity and context correlation degree are quantified. On this basis, the customer interest mining and drift detection algorithm which integrates the situation contribution degree is proposed, and the model and expression of customer interest are modeled and expressed under the influence of the situation contribution, and the situational customer preference pattern is used by the change of the confidence and support degree of the association rules. In the end, the collaborative filtering recommendation algorithm based on the project is improved, the relationship between the items in the association rules is used to find the candidate item set, and the situation contribution degree which affects the customer's interest is replaced by the score to deal with the sparsity of the data, and the accuracy of the similarity between the calculated items is improved by.4., which considers the mutation of the customer's interest. The situational recommendation method of the drift feature studies the problem of customer continuous purchase for the abrupt drift of the interest of the e-commerce platform. Considering the significant evolution of the interests that are difficult to adapt to the changes of the psychological factors such as the external situation and the user cognition in the personalized recommendation service, a new situational recommendation method is proposed. The "Maslow demand hierarchy theory" shows that human needs will change, which explains the cause of people's interest drift. According to this principle, we designed the customer's commodity or category preference, the corresponding mechanism of the purchase behavior and the customer demand level; then, the mechanism based on the above mechanism is put forward based on this mechanism. The customer interest level determination algorithm of body and hidden Markov is expressed and modeled for customer interest. Secondly, the concept of customer activity is introduced and the customer activity calculation method is put forward to solve the cold start and sparsity problems in the recommendation service. Finally, a dynamic collaborative filtering recommendation which is integrated into the customer activity is proposed. Algorithm, continuous monitoring, learning of changing rules of customer interest, selective extension of candidate recommendations, and determination of jumping interest trends to adapt to abrupt drift problem.5., a situational recommendation Application Research on e-commerce platform customers' continuous purchase problem, the model and method proposed in this study are applied to e-commerce situation. In the problem of customer continuing purchase, the system framework of the recommendation system is designed and the specific application is carried out. Through the actual data, the recommendation effect of this research method in the continuous purchase problem of a B2C e-commerce platform customer is verified and analyzed. Finally, some measures to improve the personalized recommendation quality of electronic commerce and the continuous purchase intention of customers are given. And suggestions for e-commerce enterprises personalized recommendation application research and customer retention provides a reference.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：F724.6
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