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研究生： 李坤宏
Kun-Hung Li
論文名稱： 資料挖掘技術應用於發展個人化推薦之評估研究―以國內某超級市場為例
An Evaluation Study of Applying Data Mining Techniques in Developing Personalized Recommendation
指導教授： 許秉瑜
Ping-Yu Hsu
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 企業管理學系
Department of Business Administration
畢業學年度： 91
語文別： 英文
論文頁數： 59
中文關鍵詞： 推薦準確度 、個人化推薦系統 、顧客區隔 、序列樣式挖掘 、資料挖掘
外文關鍵詞： Sequential Pattern Mining, IBM Intelligent Miner, Recommendation Accuracy, Personalized Recommender System, Behavioral Segmentation, Demographic Segmentation, Customer Segmentation, I-PrefixSpan Algorithm, Data Mining
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    • 一般而言，顧客關係管理(CRM)對企業相當有利，而首要工作即區隔顧客，以提供客製化的產品與服務。然而，市場區隔的方式有很多，如何設計適當的區隔方法相對較為重要。再者，推薦系統即用來推薦產品給顧客，並提供相關資訊以利顧客購物。如果推薦系統特別因個人而設計，則顯得較為合適且簡潔。本研究的目的，即提供個人化產品推薦的方法，以在最適當的時機推薦最適當的產品，並針對由人口統計變數與行為變數為基礎的推薦，來評估其差異。本研究運用了兩種資料挖掘的技術：以IBM Intelligent Miner來區隔顧客，並以I-PrefixSpan演算法，在每個集群挖掘出時間間隔的序列樣式。結果指出以行為變數為基礎的推薦，比人口統計的更為準確。

    It is recognized that customer relationship management (CRM) is the key point to benefit business, and the first task is to segment customers for providing customized products and services. However, the ways of market segmentation are of variety, and how to design the proper way to separate customers is more significant relatively. Also, recommender systems are used to suggest products to their customers and to provide consumers with information to help them purchase. If the recommendation is specifically designed for individuals, it will be more suitable and concise. The aims of our study are to suggest a method of personalized product recommendations to recommend appropriate products at appropriate time, and to evaluate the difference of recommendations based on demographic and behavioral segmentations. We employed two techniques of data mining: IBM Intelligent Miner to cluster the customers, and I-PrefixSpan algorithm to discover time-interval sequential patterns in every cluster. Results indicated the recommendation based on behavioral segmentation is more accurate than that based on demographic segmentation.

    1 INTRODUCTION 1 1.1 Background 1 1.2 Motivation and Objectives 3 1.3 Research Procedure 4 1.4 Thesis Organization 5 2 LITURATURE REVIEW 6 2.1 Overview of Data Mining 6 2.1.1 Definition and characteristics of data mining 6 2.1.2 Primary tasks of data mining 7 2.1.3 Clustering techniques 9 2.1.4 Sequential pattern mining techniques 12 2.1.5 Illustration of I-PrefixSpan 13 2.1.6 IBM Intelligent Miner 16 2.2 Development of Recommender System 19 2.2.1 Content-based filtering 19 2.2.2 Collaborative filtering 20 2.2.3 Taxonomy for applications to recommender system 22 2.3 Target Marketing 24 2.3.1 Market segmentation variables 24 2.3.2 RFM analysis 26 3 METHODOLOGY 28 3.1 Research Framework 28 3.2 Data Sets 30 3.3 Product Taxonomy 32 3.4 Customer Segmentation by Clustering 33 3.5 Discovery of Time-Interval Sequential Patterns 37 4 RECOMMENDATION BY DATA MINING TECHNIQUES 39 4.1 Clusters Produced by Demographic Clustering Technique 39 4.2 Clusters Produced by Neural Clustering Technique 45 4.3 Time-Interval Sequential Patterns Discovered by I-PrefixSpan 48 4.4 Personalized Product Recommendations 50 5 COMPARISON AND CONTRAST 51 6 DISCUSSION AND LIMITATION 53 REFERENCES 55

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