全球電子商務規模的規模達到每日數百億美金，帶動全球各大運輸業、品牌業以及全球供應鏈的蓬勃成長。因此，在各大線上交易平台如Amazon.com、淘寶網等積極發展推薦系統的技術來發掘消費者潛在的欲購買物。推薦系統擁有多種不同的演算法，本研究主要使用協同過濾藉利用使用者與提他相似的群體的偏好，預測使用者的個人偏好，進而達到個人化的推薦效果。其優點是不需要去分析顧客所觀看的內容，僅需分析顧客的評分。本研究使用改良漸進式最近鄰居值演算法進行協同過濾的預測，並以萬用演算法自動最佳化改良漸進式K鄰近值演算法中的參數以提高預測結果。此外，在改良漸進式最近鄰居值演算法中，將過去每新增一筆資料即進行更新的方法改良為批次進行更新，並期望能夠增加演算法的效率以及準確度。
為了驗證本研究所提出的整合預測模型是否準確，本研究使用K-fold交叉驗證，將資料分成五等分，以驗證在五個等分的資料集都能較好的預測結果。而所使用的資料集包含Movielens 100K、Movielens 1M、 Books-Crossing 及 Restaurants共四個資料集。實驗證明，以萬用演算法為基礎的改良漸進式最近鄰居值演算法，相對於基本最近鄰居值演算法以及原本的改良漸進式最近鄰居值演算法均有明顯差異。

Global e-commerce has grown very fast, and daily revenue can be up to billion US dollars. Many industries follow the trend and earn lots of money, such as: Amazon and Taobao. To raise revenue, most of e-commerce companies endeavor to develop recommendation system in order to find out potential customers or stick customers. Recommendation systems can be implemented by some methods and the most well-known method is collaborative filtering. It mainly uses similar user’s records to recommend what similar users like. Its advantage is no need to analyze the product’s profile. This study uses the boosting the K-nearest-neighbors based incremental collaborative filtering method (BIKNN) as collaborative filtering, and uses metaheuristics to optimize BIKNN’s parameters to improve prediction performance. Besides, this study uses batch updating method instead of incremental updating method to reduce the computational time.
To validate the proposed algorithm, this study conducts K-fold cross validation. Four benchmark dataset are used in the experiment: Movielens 100K, Movielens 1M, Books-crossing and Restaurants. The experimental results indicate that the three metaheuristic-based BIKNN algorithms are different and better than basic KNN and original BIKNN.

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