Using Collaborative Filtering to Enhance Domain-Independent CBR Recommender's Personalization
Abstract
Case-Based Reasoning (CBR) is a problem solving methodology that reuses the knowledge of past experiences to solve new problems. It's a knowledge-based technique that has been introduced to the recommendation field to allow reasoning on domain knowledge and to generate more accurate recommendations. If CBR helps suggesting items that meet the users' search criteria, it has the disadvantage of being domain-dependent (all the reasoning process is generally based on hard-coded domain knowledge) and generating less personalized recommendations. In this paper, we propose an approach for a generic and personalized CBR-based recommender system. First, we use a generic ontology to formalize all the knowledge required during the reasoning process. The ontology represents an intermediate layer between the recommender engine and the application domain to ensure the domain-independence criteria. Second, we propose a hybridization strategy that combines CBR and collaborative filtering to alleviate the limitations of CBR and improve the personalized character of the recommendations. Finally, preliminary validation is performed using a publicly available data set of restaurants.