194 S Schiaffino and A. amandi technique. User profiling implies inferring unobservable information about users from observable information about them, that is, their actions or utterances(Zu- kerman and Albrecht, 2001). A wide variety of Artificial Intelligence techniques have been used for user profiling, such as case-based reasoning(Lenz et al, 1998 Godoy et al., 2004), Bayesian networks(Horvitz et al, 1998; Conati et al, 2002 Schiaffino and Amandi, 2005; Garcia et al, 2007), association rules(Adomavicius and Tuzhilin, 2001; Schiaffino and Amandi, 2006), genetic algorithms(Moukas 1996, Yannibelli et al, 2006), neural networks(Yasdi, 1999, Villaverde et al, 2006), The purpose of obtaining user profiles is also different in the various areas that use them. In adaptive systems, the user profile is used to provide the adaptation effect, that is to behave differently for different users(Brusilovsky and Millan, 2007). In intelligent agents, particularly in interface agents, the user profile is used to provide personalized assistance to users with respect to some software applica- tion(Maes, 1994). In intelligent tutoring systems, the user profile or student model is used to guide students in their learning process according to their knowledge and learning styles( Garcia et al, 2007). In e-commerce applications the user or customer profile is used to make personalized offers and to suggest or recommend products the user is supposed to like( Adomavicius and Tuzhilin, 2001). In knowl edge management systems, the skills a user or employee has, the roles he takes within an organization, and his performance in these roles are used by managers or project leaders to assign him to the job position that suits him best(Sure et al 2000). In recommender systems the user profile contains ratings for items like mov ies, news or books, which are used to recommend potentially interesting items to him and to other users with similar tastes or interests(Resnick and Varian, 1997) In this Chapter we study user profiles from the different perspectives mentioned above. In Section 2 we describe what information constitutes a user profile. In Section 3 we examine the different ways in which we can acquire informatio about a user and then build a user profile. Section 4 focuses on intelligent user profiling techniques. Finally, Section 5 presents some future trends 2 User Profile contents A user profile is a representation of information about an individual user that is essential for the(intelligent)application we are considering. This section describes the most common contents of user profiles: user interests; the user's knowledge, background and skills; the user's goals; user behaviour; the users interaction preferences; the user's individual characteristics; and the users context. We ana- lyze and provide examples for the different contents in areas like intelligent agents, adaptive systems, intelligent tutoring systems, recommender systems, and knowledge management systems194 S. Schiaffino and A. Amandi technique. User profiling implies inferring unobservable information about users from observable information about them, that is, their actions or utterances (Zu￾kerman and Albrecht, 2001). A wide variety of Artificial Intelligence techniques have been used for user profiling, such as case-based reasoning (Lenz et al, 1998; Godoy et al., 2004), Bayesian networks (Horvitz et al, 1998; Conati et al, 2002; Schiaffino and Amandi, 2005; Garcia et al, 2007), association rules (Adomavicius and Tuzhilin, 2001; Schiaffino and Amandi, 2006), genetic algorithms (Moukas, 1996; Yannibelli et al, 2006), neural networks (Yasdi, 1999; Villaverde et al, 2006), among others. The purpose of obtaining user profiles is also different in the various areas that use them. In adaptive systems, the user profile is used to provide the adaptation effect, that is to behave differently for different users (Brusilovsky and Millán, 2007). In intelligent agents, particularly in interface agents, the user profile is used to provide personalized assistance to users with respect to some software applica￾tion (Maes, 1994). In intelligent tutoring systems, the user profile or student model is used to guide students in their learning process according to their knowledge and learning styles (Garcia et al, 2007). In e-commerce applications the user or customer profile is used to make personalized offers and to suggest or recommend products the user is supposed to like (Adomavicius and Tuzhilin, 2001). In knowl￾edge management systems, the skills a user or employee has, the roles he takes within an organization, and his performance in these roles are used by managers or project leaders to assign him to the job position that suits him best (Sure et al, 2000). In recommender systems the user profile contains ratings for items like mov￾ies, news or books, which are used to recommend potentially interesting items to him and to other users with similar tastes or interests (Resnick and Varian, 1997). In this Chapter we study user profiles from the different perspectives mentioned above. In Section 2 we describe what information constitutes a user profile. In Section 3 we examine the different ways in which we can acquire information about a user and then build a user profile. Section 4 focuses on intelligent user profiling techniques. Finally, Section 5 presents some future trends. 2 User Profile Contents A user profile is a representation of information about an individual user that is essential for the (intelligent) application we are considering. This section describes the most common contents of user profiles: user interests; the user’s knowledge, background and skills; the user’s goals; user behaviour; the user’s interaction preferences; the user’s individual characteristics; and the user’s context. We ana￾lyze and provide examples for the different contents in areas like intelligent agents, adaptive systems, intelligent tutoring systems, recommender systems, and knowledge management systems