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.
