A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals

作者：Jesus Castro Schez Jose; Miguel Raul; Vallejo David; Manuel Lopez Lopez Lorenzo

来源：Expert Systems with Applications, 2011, 38(3): 2441-2454.

DOI：10.1016/j.eswa.2010.08.033

• e-Commerce
• C2C
• B2C
• Product selection
• Recommender system
• Supervised learning
• Fuzzy logic
• Rule-based knowledge learning

Past years have witnessed a growing interest in e-commerce as a strategy for improving business. Several paradigms have arisen from the e-commerce field in recent years which try to support different business activities, such as B2C and C2C. This paper introduces a prototype of e-commerce portal, called e-Zoco. of which main features are: (i) a catalogue service intended to arrange product categories hierarchically and describe them through sets of attributes, (ii) a product selection service able to deal with imprecise and vague search preferences which returns a set of results clustered in accordance with their potential relevance to the user, and (iii) a rule-based knowledge learning service to provide the users with knowledge about the existing relationships among the attributes that describe a given product category. The portal prototype is supported by a multi-agent infrastructure composed of a set of agents responsible for providing these and other services.

• 出版日期2011-3