ino and A. Amandi OOT User Topics ( Relevance 0.1) nampionship 0.9 imbledom 0.7 ATP ser Reading Fig. 1. Hierarchical representation of a user's interests 2.2 Knowledge, background and skills The knowledge the user has about the application domain, his background experi ence and his skills are important features within user profiles in different areas. In intelligent tutoring systems and adaptive educational systems, the students knowledge about the subject taught is vital to provide proper assistance to the student or to adapt the content of courses according to it. This knowledge can be represented in different ways. The most common representation is through a model that keeps track of the student knowledge about every element in the cours knowledge base. The idea is to mark each knowledge item X with a value calcu- lated as"student knowledge of X The value could be binary(knows-does not know), qualitative(good-average -bad)or quantitative, assigned as a probability of the student's familiarity with the item X. For instance, in Cumulate(Brusi lovsky et al, 2005), the state of a student s knowledge is represented as a weighted overlay model covering a set of topics, and each educational activity can contrib- ute to only one topic Another way of representing user's knowledge is through errors or misconcep- ions. In addition to(or instead of) modelling what the user knows, some works focus on modelling what the user does not know. For example, in( Chen and Hsieh 2005)the authors aim at diagnosing learners'common learning misconcep- tions during learning processes. They try to discover relationships between mis- conceptions Also, in many applications, the user's knowledge about the underlying domain is important. Some systems categorize users as expert, intermediate, or novice, depending on how well they know the application domain. For example, MetaDoc (Boyle and Encarnacion, 1994 )considers the knowledge users have about Unix, which is the underlying application domain in this system196 S. Schiaffino and A. Amandi ROOT (Relevance 0.5) economy finances dollar 0.9 0.8 0.8 (Relevance 0.7) championship team player 0.9 0.8 0.7 (Relevance 0.1) politics vote president 0.8 0.9 0.7 (Relevance 0.4) tennis Wimbledom ATP 1.0 0.7 0.9 (Relevance 0.3) football world-cup FIFA 1.0 0.8 0.8 User Reading Experiences User Topics of Interest Fig. 1. Hierarchical representation of a user’s interests 2.2 Knowledge, background and Skills The knowledge the user has about the application domain, his background experi￾ence and his skills are important features within user profiles in different areas. In intelligent tutoring systems and adaptive educational systems, the student’s knowledge about the subject taught is vital to provide proper assistance to the student or to adapt the content of courses according to it. This knowledge can be represented in different ways. The most common representation is through a model that keeps track of the student knowledge about every element in the course knowledge base. The idea is to mark each knowledge item X with a value calcu￾lated as “student knowledge of X”. The value could be binary (knows - does not know), qualitative (good - average - bad) or quantitative, assigned as a probability of the student’s familiarity with the item X. For instance, in Cumulate (Brusi￾lovsky et al, 2005), the state of a student’s knowledge is represented as a weighted overlay model covering a set of topics, and each educational activity can contrib￾ute to only one topic. Another way of representing user’s knowledge is through errors or misconcep￾tions. In addition to (or instead of) modelling what the user knows, some works focus on modelling what the user does not know. For example, in (Chen and Hsieh 2005) the authors aim at diagnosing learners’ common learning misconcep￾tions during learning processes. They try to discover relationships between mis￾conceptions. Also, in many applications, the user’s knowledge about the underlying domain is important. Some systems categorize users as expert, intermediate, or novice, depending on how well they know the application domain. For example, MetaDoc (Boyle and Encarnacion, 1994) considers the knowledge users have about Unix, which is the underlying application domain in this system