考量到不同資料探勘技術所展現出來的成效也許會有差異，因此本研究將同時使用線性回歸與類神經網路兩種演算法，來建立一套新的混合推薦系統預測模型，並比較不同推薦技術之間的成效，找出最佳的混合推薦模型。
Nowadays, the Recommender System has been developed in several different ways for operating. The main techniques are used to develop Recommender System: CB (Content-Based), CF (Collaborative Filtering) and DF (Demographic Filtering). However, each technique has its advantages and limitations. For this reason, many scholars have proposed combine several techniques, intended to reduce the disadvantages of a single method, and achieve more precise recommendation.
Currently, the main techniques are used to develop Recommender System, mostly according to the experience of the past research or heuristic method. It lacks of rigorous theoretical foundation. Therefore, this study hopes to use the concepts of CB and CF, plus DF techniques, combining the Data Mining techniques (i.e., Linear Regression, Neural Networks) with the predication. To sum up, it will provide a more accurate prediction than one single technique, and overcome the limitations of each respective potential problem.
