The implementation of knowledge-based recommender system

The mainstream of recommender systems rely on collaborative or content-based filtering. This paper proposes a knowledge -based recommender system that interacts with the user and matches his/her requirements based on knowledge of the product domain using an inference engine (deduction based on rules). The knowledge base of the product and inference engine is implemented by JESS and java servlet. This approach requires a domain expert to model the object to macth with user requirements. After finding candidate products, cosine is used to measure similarity between the requirements and the products' features. The paper presents a sample implementation of a cellular phone recommender that uses phone model.
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