Context enabled multi-CBR based recommendation engine for e-commerce

Electronic commerce is steadily becoming more important in changing the way people buy/sell products and services. Case-based reasoning (CBR) has been used in various e-commerce applications for product recommendations. The appropriateness of the use of CBR in e-commerce applications is enhanced by introducing context-sensitive information related to e-commerce as cases in CBR. Usage of context leads to providing the right level of information to users in assisting them to take right decisions quickly. In this paper, we have proposed a context enabled multi-CBR approach comprising of two CBRs (user context CBR and product context CBR) to aid the recommendation engine (RE) in retrieving appropriate information for e-commerce applications. The RE further derives personalized negotiation and presentation strategies based on contextual information and ontology