Semantic-Enhanced Case-Based Reasoning for Intelligent Recommendation
Huimin Wang, Guihua Nie, Donglin Chen · 2009
Case-based reasoning techniques have been applied to recommender systems. In this paper, we have presented a new intelligent recommendation approach that combines semantic Web techniques with case- based recommendation techniques to improve the performance of recommender systems. The proposed case model integrates both content information and rating information. Instead of using syntactic techniques, case similarity between the current case and a retrieved case is measured based on semantic similarity algorithm in order to understand and reuse cases well stored in distributed case bases. The domain ontologies provide a formal representation which includes semantic descriptions of users and products. The proposed approach that considers semantic information of both the products' content descriptions and the user's preferences overcomes the limitations of the traditional recommender systems.