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系統識別號 U0026-0408201514141500
論文名稱(中文) 基於購物決策過程挖掘之購買時機預測
論文名稱(英文) Purchase Timing Prediction by Mining Consumer Decision-Making Process
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 103
學期 2
出版年 104
研究生(中文) 張漢斌
研究生(英文) Hanbin Zhang
學號 P76023029
學位類別 碩士
語文別 英文
論文頁數 61頁
口試委員 指導教授-謝孫源
口試委員-曾新穆
召集委員-洪宗貝
口試委員-莊坤達
中文關鍵字 購買時機預測  生存分析  創新擴散理論  購買決策過程  推薦系統  資料探勘 
英文關鍵字 purchase timing prediction  survival analysis  diffusion of innovation theory  consumer decision-making process  recommender system  data mining 
學科別分類
中文摘要 近年來,隨著電子商務服務的進步,推薦系統得到了大量的關注。雖然已經有不少的研究探討此議題,但其中的多數只考慮“顧客會買什麼”的問題。做正確的事是不夠的,還需要挑選正確的時機才能把事情做正確。對於提升系統效能和顧客滿意度也同樣重要的“顧客何時會買”的問題,雖然也得到了一定的關注,但並沒有研究嘗試通過分析顧客的決策心理活動過程來解決該問題。本研究提出一種新型的雙層架構,能有效地通過對顧客的購買決策過程(Consumer Decision-Making Process)的挖掘,實現對購買時機的預測。在該架構的第一層,我們首先根據營銷學的著名理論——創新擴散理論(Diffusion of Innovation Theory),對顧客進行分類。分類的依據是其首次有效登錄時間對應的商品生命週期。性格和消費習慣不同的顧客將被分入不同的組別,從而使得最終獲得的模型更具代表性,購物時間預測更準確。在分類完成後,架構的第二層將分別對各組顧客的行為記錄和購買記錄進行挖掘。該層架構的核心,是量化同一類顧客在購買決策過程中相關特徵參數對於購買時機的影響。為此,我們引入了在醫學領域已經廣泛使用的生存分析。利用生存分析得到購買行為的生存函數,進而實現對顧客的購買時機進行預測。據我們所知,本研究是第一個在解決購買時機預測的問題上,同時將創新擴散理論和購買決策過程納入考量並通過生存分析對其進行挖掘之研究。在YooChoose所提供的真實網路購物資料的基礎之上,我們對部分特徵參數和購買時機進行了模擬。基於這份半模擬的實驗資料,我們進行了一系列的實驗。實驗結果顯示,本研究所提出之新架構方法可以對顧客的購買時機做出有效的預測。
英文摘要 With the advances of the e-commerce service in recent years, the recommender system has received lots of attention. While there are many researchers being interested in this area, most of them only focused on the problem of “what will consumers buy”. However, doing the right thing is not enough. Only by doing it at the right moment can we make it right. As a key to improving the performance of the entire recommender system, “when will consumers buy” has received a few attention but few of them tried to solve it by analyzing consumers’ psychological decision-making process. This research proposes a novel two-stage framework that can effectively predict the purchase timing by mining the consumer decision-making process. In the first stage, according to the diffusion of innovation theory that is widely adopted in the marketing science, we would categorize consumers into different groups. The categorization is decided by the phase of product life cycle at which consumers’ first effective login happen. Since consumers of different characteristics and consuming habits would be categorized into corresponding groups, the final model can be more representative and the predicted timing would be more accurate. Then, the second stage starts to mine the activity logs of each group. Quantifying the effects of explanatory features on purchase timing is the core target of this stage. We utilize the survival analysis, which has been widely used in the medical science, to produce the survival function of purchase actions and predict the purchase timing. As far as we are concerned, in the area of purchase timing prediction, this research is the first one that takes both diffusion of innovation theory and consumer decision-making process into consideration, as well as exploits the survival analysis to analyze the decision-making process. Based on the real e-commerce data provided by the YooChoose, we simulated some necessary features and the purchase timing. We conducted a series of experiments on that semi-simulated data and the evaluation results showed that our proposed framework is able to predict consumers’ purchase timing effectively.
論文目次 中文摘要 I
Abstract III
誌謝 V
Content VI
List of Tables VIII
List of Figures IX
1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Problem Statement 5
1.4 Research Aims 7
1.5 Thesis Organization 7
2. Related Work 8
2.1 Traditional Recommender Systems 8
2.1.1 Content-Based Filtering 9
2.1.2 Collaborative Filtering 11
2.1.3 Limitations of Traditional Recommender Systems 12
2.2 Recommender Systems Incorporating Temporal Effects 13
2.2.1 What You Will Buy 13
2.2.2 When You Will Buy 14
3. Proposed Methods 16
3.1 Overview of Our Proposed Framework 16
3.1.1 Off-Line Mining System 16
3.1.2 On-Line Predicting System 18
3.2 Consumer Grouping 19
3.2.1 Scenario 19
3.2.2 Diffusion of Innovations Theory 21
3.2.3 Design Logic and Implementation Details 28
3.2.3.1 Design Logic 28
3.2.3.2 Implementation Details 31
3.3 Purchase Timing Prediction 32
3.3.1 Consumer Decision-Making Process 33
3.3.2 Survival Analysis 35
3.3.2.1 Design Logic 36
3.3.2.2 Models 36
4. Experiments and Evaluation 40
4.1 Semi-Simulated Data 40
4.1.1 Real Part 41
4.1.1.1 Basic Information 41
4.1.1.2 Data Description 41
4.1.1.3 Data Extraction 42
4.1.2 Simulated Part 43
4.1.2.1 Features 43
4.1.2.2 Consuming Habits 44
4.1.2.3 Purchase Timing 45
4.2 Experimental Evaluation 47
4.2.1 Comparison Targets and Metrics 47
4.2.2 Experimental Results 49
4.2.2.1 Category-I Consumers 49
4.2.2.2 Category-II Consumers 51
4.2.2.3 Mixed Consumers 52
4.3 Summary of Experimental Results 53
5. Conclusion and Future Works 55
5.1 Conclusion 55
5.2 Future Works 57
References 58
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