Product hierarchy-based customer profiles for electronic commerce recommendation

Personalized service is becoming a key strategy in electronic commerce. Traditional personalization techniques such as collaborative filtering and rule-based method have many drawbacks, including lack of scalability, reliance on subjective user rating or static profiles, and the inability to capture a richer set of semantic relationships among objects. In this paper, we present a new approach by building customer profiles based on the product hierarchy for more effective personalization in electronic commerce. We divide each customer profile into three parts: the basic profile, preference profile, and rule profile. Based on the customer profiles, two kinds of recommendations can be generated: interest recommendation and association recommendation. We also propose a special data structure: a profile tree for effective searching and matching. By using our method, customer profiles can be constructed online, and real-time recommendations can be implemented. Finally, we conducted experiments to validate our methods using real data.