| Summary: | 碩士 === 淡江大學 === 資訊工程學系碩士班 === 102 === 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.
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