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Intelligent Personalization Agent for Product Brokering
Abstract
A good business to consumer environment can be developed through the creation of intelligent software agents (Guan, Zhi, & Maung, 2004; Soltysiak & Crabtree, 1998) to fulfill the needs of consumers patronizing online e-commerce stores (Guan, 2006). This includes intelligent filtering services (Chanan, 2001) and product brokering services (Guan, Ngoo, & Zhu, 2002) to understand a user’s needs before alerting the user of suitable products according to his needs and preference. We present an approach to capture user response toward product attributes, including nonquantifiable ones. The proposed solution does not generalize or stereotype user preference but captures the user’s unique taste and recommends a list of products to the user. Under the proposed approach, the system is able to handle the inclusion of any unaccounted attribute that is not predefined in the system, without reprogramming the system. The system is able to cater to any unaccounted attribute through a general description field found in most product databases. This is useful, as hundreds of new attributes of products emerge each day, making any complex analysis impossible. In addition, the system is selfadjusting in nature and can adapt to changes in user preference.
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