應用屬性加權式分群法於推薦系統之研究

Abstract

在電子商務的市場行銷，決策者會根據不同的策略主題(Strategy Subjects)，例如市場佔有率、最大利潤等等不同的主題來訂定行銷的策略。因此以資料探勘之分群技術進行市場行銷分析時，須考量產品類別、價位及消費者的基本資料等屬性，對於不同行銷策略的權重影響。本研究提出加權式分群法，考量屬性之權重分佈以進行不同策略主題之分析。本文以一電子商務的實例資料集，針對商品的市場佔有率、商品獲利率以及商品推薦為策略主題，進行實驗以驗證加權式分群法之價值

In managing marketing activities, enterprises usually make marketing decisions according to different strategy subjects such as market holding ration or maximum profit. The weightings of features, including product category, unit price and customer data, may vary for different strategy subjects. Accordingly, applying data mining technique such as clustering to market analysis needs to consider the weightings of features. This work proposed a feature-weighting clustering approach to analyze various marketing strategy subjects via considering the weightings of features. Experimental evaluations were conducted to evaluate the effect of the proposed approach on three strategy subjects including market holding ratio, sale revenue and product recommendations.