由於電子商務盛行，企業必須在更短的期間內提供更多的產品與服務給消費者才能為持競爭優勢。然而消費者暴露在過多的資訊下往往無法做出正確的決策，為了改善資訊爆炸的問題，推薦系統因而被企業大量採用。 一個良好的推薦系統必須具備互動性、適應性、並且具備足夠的正確性。然而目前大部分的推薦系統並不能滿足上述所有的條件。由於缺乏與消費者間的互動，現行的推薦系統無法及時修正推薦方向來滿足消費者的需求變動。一但消費者在作決策時無法獲得足夠的資訊，他們將不會感到滿意。 在本研究中，我們想要介紹一個理想推薦系統，並以KTV推薦點歌服務為例。我們提出一個新的方法，根據資料挖掘出的關聯規則建立起概念階層，並根據消費者的決策在概念階層中上升與下降移動，並即時地修正推薦的方向，如此我們能夠達成大量推薦的目的並維持一定的推薦正確性。 For KTV, virtual storefronts and many other industries, the recommendation systems have to be interactive, adaptive and accurate enough since customers make series of decisions quickly. A system slowly adapt to customers need may find customers make all decisions before the system can react. Therefore, an ideal recommendation system for customers who make a set or series of decisions quickly should have following characteristics: interactive, adaptive, accurate enough, bulk recommendations. However, most recommender systems can’t meet all conditions. Because of the lack of interacting with customers, current recommender systems can not adapt to customers in real time. Once, customers can not obtain useful information when making decision and they would never be satisfied. In this paper, we want to introduce an ideal recommender system applied to KTV server. We propose a new method to produce recommendations based on a context hierarchy for association rules which are discovered from picking historical data. By rolling up and drilling down the context level, we are able to make bulk recommendations. After recommending, we measure the accuracy of suggestion for quickly adaptive to customers.
