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研究生:李坤宏
研究生(外文):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
論文名稱(外文):An Evaluation Study of Applying Data Mining Techniques in Developing Personalized Recommendation
指導教授:許秉瑜許秉瑜引用關係
指導教授(外文):Ping-Yu Hsu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:企業管理學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:英文
論文頁數:59
中文關鍵詞:資料挖掘序列樣式挖掘顧客區隔個人化推薦系統推薦準確度
外文關鍵詞:Recommendation Accuracy.Personalized Recommender SystemBehavioral SegmentationDemographic SegmentationCustomer SegmentationI-PrefixSpan AlgorithmIBM Intelligent MinerSequential Pattern MiningData Mining
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  • 被引用被引用:2
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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.
1INTRODUCTION1
1.1Background1
1.2Motivation and Objectives3
1.3Research Procedure4
1.4Thesis Organization5
2LITURATURE REVIEW6
2.1Overview of Data Mining6
2.1.1 Definition and characteristics of data mining6
2.1.2 Primary tasks of data mining7
2.1.3 Clustering techniques9
2.1.4 Sequential pattern mining techniques12
2.1.5 Illustration of I-PrefixSpan13
2.1.6 IBM Intelligent Miner16
2.2Development of Recommender System19
2.2.1 Content-based filtering19
2.2.2 Collaborative filtering20
2.2.3 Taxonomy for applications to recommender system22
2.3Target Marketing24
2.3.1 Market segmentation variables24
2.3.2 RFM analysis26
3METHODOLOGY28
3.1Research Framework28
3.2Data Sets30
3.3Product Taxonomy32
3.4Customer Segmentation by Clustering33
3.5Discovery of Time-Interval Sequential Patterns37
4RECOMMENDATION BY DATA MINING TECHNIQUES39
4.1Clusters Produced by Demographic Clustering Technique39
4.2Clusters Produced by Neural Clustering Technique45
4.3Time-Interval Sequential Patterns Discovered by I-PrefixSpan48
4.4Personalized Product Recommendations50
5COMPARISON AND CONTRAST51
6DISCUSSION AND LIMITATION53
REFERENCES55
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