近年來電子商務的興起，人們的消費行為也逐漸在改變，從傳統的實體店面購物轉變為更便捷的網路電子購物，這種消費行為的改變使的傳統實體零售業(Retail)在銷售宣傳的模式上必須有所改變。由於Email Flyers(在台灣稱作Electronic Direct-Mail)的部分，成本較低廉，因此很多傳統實體零售業會藉由大量的發送電子促銷DM來吸引顧客回到門市進行消費，將最近的促銷品皆放入電子DM之中。就消費者行為而言，當顧客回到門市進行消費時，可能會購買不在預定購買清單或是電子DM中的商品，所以發送電子DM的主要目的是藉由DM吸引顧客回到門市進行消費。但電子DM中卻不一定都是顧客所喜好的商品，將過多種類的商品放入於電子DM以及發送次數過於頻繁時，將導致顧客必須使用相當多的時間來閱讀電子DM才能找到自己所喜好的商品；可能會導致顧客對於電子DM的觀感變差，產生厭惡感，因此無法吸引顧客回到門市進行消費。在本研究中，將利用協同過濾推薦系統的分析，設計一個適合以食品為主超市的推薦演算法，依據Cross-selling的概念，針對『顧客已經購買過的商品』和『顧客未購買過的商品』兩個因素進行考量，將顧客最有可能購買的商品製作成客製化電子DM，藉此吸引顧客回到門市進行消費。因此，本研究中所提出的推薦演算法除了能顧及顧客原有的喜好，藉此吸引顧客回到門市進行消費外，還能將更多顧客購買先前未購買過的商品推薦給顧客，以增加超市收益。 With the rise of e-commerce in recent years, most people have changed their purchase behavior. Instead of going to the physical stores shopping, people prefer to buy things online conveniently. These changes of purchase behavior causing traditional retail trade must change the way their advertising pattern. Due to the cheaper cost of email flyer (or electronic Direct-Mail), traditional retail trade would send lots of e-DM to attract customers to return back to physical retail store and put promotion merchandise into e-DM to attract customers to return to physical stores. When customers return to stores and buy merchandises, they probably would buy some merchandise that weren’t on the shopping list or on the e-DMs, therefore, the main purpose sending e-DM is attracting customers back to stores and purchase merchandises. However, not every merchandises on e-DM was customers’ favorite merchandise, putting too many kinds of merchandise or sending too many e-DMs would make customers spend too much time on finding their favorite merchandise on e-DMs. This might be leaving customers a bad impression, and stopped them from returning to the stores for shopping. In this paper, we would design a proper algorithm by analyzing Collaborative filtering recommender system for supermarket. According to the concept of cross-selling, we would consider these two factors, the merchandise that customers had bought and the merchandise that they hadn’t bought, and choose the most possible merchandise to make customize e-DM to attract customers return to store and purchase merchandise. Therefore, the algorithm we designed in this article could not only considering the customers’ purchase behavior to attract them return to store and purchase merchandise but also recommending the merchandise that customers hadn’t bought before to increase revenue for supermarket.