The Timely Recommendation for Product Purchasing Period Based on RFM Method

• Main Authors: Wan-Jing Liu, 劉宛晶
• Other Authors: Li-Hua Li
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/yn685r

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 94 === The recent study of recommendation systems and RFM method has been applied to analyze customers’ consumption property and the re-purchasing ability. The RFM method employs Recency (R), Frequency (F), and Monetary (M) to measure customers’ consumption loyalty. The recommendation systems are mainly to promote products for increasing profit. However, there are some problems because these approaches ignore the relationship between product property and purchase periodicity. That is, the combination of recommendation systems and RFM method did not take the customers product-purchasing timing into consideration. If the periodicity of product-demand can be estimated by each customer’s buying behavior, then the product recommendation at the right timing shall match the buying requirement. This is the reason why the past product recommendation studies have difficulty of increasing the accuracy. To deal with the product periodicity, this research proposes a Timely RFM (TRFM) method which takes product property and purchase periodicity into consideration. This method uses Adaptive Resonance Theory (ART) of Artificial Neural Network (ANN) to cluster customers based on their purchasing behavior in order to obtain similar interest of customer. This research is intended (1) to analyze different products to each customer’s demands in different times, (2) to provide a recommendation mechanism to satisfy customers’ needs, and (3) to improve the deficiency of existing combination with recommendation and RFM. To examine the practicability and to validate the method, the experimentation uses the Foodmart2000 database of Microsoft SQL2000 to verify the accuracy of TRFM. The results prove that our proposed method can provide a timely recommendation and create better results than non-timely recommendation.