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|Other Titles: ||Design of supermarket recommender system|
|Authors: ||張淑俐;Chang, Shu-Li|
|Keywords: ||推薦系統;協同過濾;Recommender System;Collaborative Filtering|
|Issue Date: ||2014-01-23 14:35:30 (UTC+8)|
Due to the rise of e-commerce in recent years, people’s consumption patterns have also changed from traditional physical storefront shopping to online shopping consumption. However, TRhe recommender system has a lot of applications in e-commerce and has also had some measure of success, but in physical retail, business firms never set the threshold for consumer spending and disregard whether or not members are able to purchase goods. Thus, business firms can only analyze members’ consumer behavior, but as non-members cannot be effectively controlled.
This research on the conceptual application of the recommender system in physical retail was conducted using the item-based method often seen in collaborative filtering, and the feasibility of indiscriminately applying the recommender system in the small categories of recommended commodities was evaluated. We use a well-known enterprise data operations provided experimental data sets,it is hoped that the sales of the commodities can be strengthened with the help of the recommender system, thereby generating consumers’ reliance, improving the degree of loyalty, and reducing the loss o f customers. In terms of the manufacturers, all the categories of consumers’ purchases can also be even more effectively grasped, and the common marketing, information cross-application, product integration, and other marketing strategies will increase consumers’ subsequent purchase possibilities, attract and keep customers, and increase the market share.
Therefore, the recommendation methods of this research can adequately provide retail a basis for marketing analysis, transform consumption patterns and thinking, and enable members to exactly obtain the appropriate recommendations, thus strengthening supermarkets’ operations and profits.
|Appears in Collections:||[資訊工程學系暨研究所] 學位論文|
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