ntelligent User Profiling Furthermore, user skills are key in areas like Knowledge Management. Within this area, skill management systems serve as technical platforms for mostly, though not exclusively, corporate-internal market places for skills and know-ho The systems are typically built on top of a database that contains profiles of em- ployees and applicants. In this domain, profiles consist of numerous values for different skills and may be represented as vectors. In( Sure et al, 2000)authors use he integers0”( no knowledge),l"( beginner),“2”( intermediate)and“3”(ex pert) as skill values. Examples of skills can be"Programming in Y or"Admini- stration of server x” Finally, the user's background refers to those user's characteristics that are not directly related to the application domain. For instance, if we consider a tutoring system, the user's job or profession, his work experience, his traveling experience. the languages he speaks, among other information, constitute the users back ground. As an application example, in( Cawsey et al, 2007) the authors describe an adaptive information system in the healthcare domain that considers users iteracy and medical background to provide them information that they can under- stand. The representation of users' background and skills is commonly done via stereotypes. We discuss them in Section 3. 4 2.3 Goals Goals represent the users objective or purpose with respect to the application he is working with, that is what the user wants to achieve. Goals are target tasks or subtasks at the focus of a users attention(Horvitz et al, 1998). If the user browsing the Web, his goal is obtaining relevant information( this type of goal is known as an information need). If the user is working with an e-learning system, his goal is learning a certain subject. In a calendar management system, the user's goals are scheduling new events or rescheduling conflicting events Determining what a user wants to do is not a trivial task. Plan recognition is a technique that aims at identifying the goal or intention of a user from the tasks he performs. In this context, a task corresponds to an action the user can perform in the software application, and a goal is a higher level intention of the user, which will be accomplished by carrying out a set of tasks. Systems using plan recogni- tion observe the input tasks of a user and try to find all possible plans by which the observed tasks can be explained. These possible explanations or candidate plans are narrowed as the user continues performing further tasks. Plan recognition has been applied in different areas such as intelligent tutoring( Greer and Kohenn, 95), interface agents (Lesh et al, 1999, Armentano and amandi, 2006), and collaborative planning(Huber and Durfee, 1994) Goals or intentions can be represented in different ways. Figure 2 shows a Bayesian network representation of a user's intentions in a calendar domain(Ar mentano and Amandi, 2006). In this representation, nodes represent user tasks and arcs represent probabilistic dependencies between tasks. Given evidence of a task performed by the user, the system can infer the next(most probable)task, andIntelligent User Profiling 197 Furthermore, user skills are key in areas like Knowledge Management. Within this area, skill management systems serve as technical platforms for mostly, though not exclusively, corporate-internal market places for skills and know-how. The systems are typically built on top of a database that contains profiles of em￾ployees and applicants. In this domain, profiles consist of numerous values for different skills and may be represented as vectors. In (Sure et al, 2000) authors use the integers “0” (no knowledge), “1” (beginner), “2” (intermediate) and “3” (ex￾pert) as skill values. Examples of skills can be “Programming in Y” or “Admini￾stration of Server X”. Finally, the user’s background refers to those user’s characteristics that are not directly related to the application domain. For instance, if we consider a tutoring system, the user’s job or profession, his work experience, his traveling experience, the languages he speaks, among other information, constitute the user’s back￾ground. As an application example, in (Cawsey et al, 2007) the authors describe an adaptive information system in the healthcare domain that considers users’ literacy and medical background to provide them information that they can under￾stand. The representation of users’ background and skills is commonly done via stereotypes. We discuss them in Section 3.4. 2.3 Goals Goals represent the user’s objective or purpose with respect to the application he is working with, that is what the user wants to achieve. Goals are target tasks or subtasks at the focus of a user’s attention (Horvitz et al, 1998). If the user is browsing the Web, his goal is obtaining relevant information (this type of goal is known as an information need). If the user is working with an e-learning system, his goal is learning a certain subject. In a calendar management system, the user’s goals are scheduling new events or rescheduling conflicting events. Determining what a user wants to do is not a trivial task. Plan recognition is a technique that aims at identifying the goal or intention of a user from the tasks he performs. In this context, a task corresponds to an action the user can perform in the software application, and a goal is a higher level intention of the user, which will be accomplished by carrying out a set of tasks. Systems using plan recogni￾tion observe the input tasks of a user and try to find all possible plans by which the observed tasks can be explained. These possible explanations or candidate plans are narrowed as the user continues performing further tasks. Plan recognition has been applied in different areas such as intelligent tutoring (Greer and Kohenn, 1995), interface agents (Lesh et al, 1999; Armentano and Amandi, 2006), and collaborative planning (Huber and Durfee, 1994). Goals or intentions can be represented in different ways. Figure 2 shows a Bayesian network representation of a user’s intentions in a calendar domain (Ar￾mentano and Amandi, 2006). In this representation, nodes represent user tasks and arcs represent probabilistic dependencies between tasks. Given evidence of a task performed by the user, the system can infer the next (most probable) task, and