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。本研究使用限制波茲曼機器進行協同過濾的預測，並以差分進化演算法最佳化限制波茲曼機器中的參數以提高預測結果。

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’s companies endeavor to develop recommendation system to find out potential customers or stick customers. Recommendation systems can be implemented by lots of 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 restricted Boltzmann machine (RBM) as collaborative filtering, and use differential evolution algorithm to optimize RBM’s parameter to improve prediction performance. Previously, original RBM use mini-batch gradient descent method.

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