基於RFM分析法之顧客適性化產品推薦機制

A Timely Adaptive Product Recommendation System Based on RFM

為了解顧客喜好並滿足其需求，企業須經常仰賴推薦系統來提供符合顧客個人化之產品或服務。過去有關個人化產品推薦研究中，以運用RFM方法及顧客的購買時間做為預測推薦基礎的研究有適時性推薦（Timely Recommendation），但是當產品遇到購買週期的天數重覆時，系統仍會予以推薦，因此這類重覆推薦將降低推薦的成效。爾後亦有研究運用產品週期性推薦，這主要是利用產品被購買的最小與最大天數區間作為推薦顧客產品之依據。不過上述這些研究均以高忠誠度即購買頻率高的顧客為主要對象，忽略低忠誠度顧客對產品可能有興趣但卻未購買的潛在偏好之重要性。有鑑於此，本研究同時考慮產品購買週期與顧客消費特性，提出一套基於RFM分析法之顧客適性化產品推薦機制。由實驗結果顯示本研究之推薦機制能提供更有效益的產品推薦。

In order to understand customer preferences and satisfy their needs, the enterprises usually rely on recommendation system (RS) to provide personalized products or service. In the past, a type of recommendation, called timely recommendation was proposed, which combined the RFM analysis and the purchased time into RS. However, this approach did not take the purchase periodicity into account. In this case, it will produce redundant recommendation and, hence, could decrease the recommendation performance. Another method of products recommendation is the product periodicity recommendation (PPR). This method takes the minimum and the maximum days of product being purchased as a basis for recommendation. However these studies ignored the importance of the potential preferences of low-loyalty customers. For this reason, this study proposes an adaptive product recommendation system (APRS) based on RFM Method. The proposed method considers both the product purchased periodicity and the characteristics of customer consumption period. The results of this research show that the proposed recommendation mechanism of this study can provide more effective product recommendation.