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, building customer profiles based on product hierarchy for more effective personalization in electronic commerce. We divide each customer profile into three parts: basic profile, preference profile, and rule profile. Based on the customer profiles, two kinds of recommendations can be generated, which are interest recommendation and association recommendation. We also propose a special data structure: Profile Tree for effective searching and matching. In terms of our method, customer profiles can be constructed online, and realtime recommendations can be implemented. In the end, we conduct experiments to validate our methods, using real data.