An associative classification-based recommendation system for personalization in B2C e-commerce applications
It has been recognized that e-commerce and mass customization will emerge as a primary style of manufacturing. The main challenge for such a paradigm originates from difficulties in personalization  providing support for customers to find the most valuable products that match their heterogeneous needs. Traditional approaches to the personalization problem adopt pre-defined formats to describe the customer requirements. This always leads to distortion in eliciting requirement information and thus inaccurate recommendations. Knowledge discovery lends itself to dealing with semi-structured data and makes it possible to capture customer requirements more accurately. This paper proposes an associative classification-based recommendation system for personalization in B2C e-commerce applications. Knowledge discovery techniques are applied to support personalization according to an inner established model that anticipates customer heterogeneous requirements. The framework and methodology of the associative classification-based recommendation system have been addressed. The system analysis, design, and implementation issues in an Internet programming environment are presented. The feasibility of the proposed recommendation system has been validated with a prototype for personalization in mobile phone B2C e-commerce applications.
recommendation system personalization e-commerce advisory system collaborative filtering content-based filtering text mining