Selecting and Applying Recommendation Technology

This paper presents a taxonomy of recommender systems with the goal of assisting in selection and application of these systems. Recommendation methods are usually classified into three main categories: collaborative,contentbased, and knowledge-based. We outline a taxonomy of recommender systems based on problem characteristics and the underlying technology. We show how the taxonomy can help researchers and developers select between different kinds of recommender systems by clearly defining the problem characteristics including: problem structure, domain, relationship with the user, user input, background knowledge, and recommendation outputs. Author