Abstract
Nowadays, Electronic Commerce (EC) provides a new gateway for customers shopping online. One of the most significant advantages offered by online shops is convenience. Online shopping is no longer a time-consuming task and, in fact, is an energy-saving activity. Therefore, shortening customers' product searching time is the key to an online shop's success. In order to serve customers instantly and efficiently, it is essential to recognize each customer's unique and particular needs and recommend a personalized shopping list. In this paper, we construct a recommendation system based on a modified product taxonomy and customer classification to identify customers' shopping behavior: product addictive, brand addictive or a hybrid addictive. By analyzing each customer's preferred brand or product, our proposed system can recommend products to customers either at the general or at the specific levels.
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