and the moment when each query is generally made. The agent detects hourly. daily, weekly, and monthly behavioural patterns 2.5 Interaction Preferences a quite new component of a user profile is interaction preferences, that is, infor mation about the user's interaction habits and preferences when he interacts with an interface agent(Schiaffino and Amandi, 2006). In interface agent technology, it is vital to know which agents actions the user expects in different contexts and the modality of these actions. A user may prefer warnings, suggestions, or actions on the user's behalf. In addition, the agent can provide assistance by interrupting or not interrupting the user's work. A user interaction preference then expresses the preferred agent action and modality for different situations or contexts. As an illustration, consider an agent helping a user, John Smith, organize his calendar Smiths current task is to schedule a meeting with several participants for the following Saturday in a free time slot. From past experience, the agent knows that one participant will disagree with the meeting date, because he never attends Sat- urday meetings. The agent can: warn the user about this problem, suggest another meeting date that considers all participant preferences and priorities, or do noth- ing. In this situation, some users would prefer a simple warning, while others would want suggestions about an alternative meeting date. In addition, when pro- viding user assistance, agents can either interrupt the users work or not. The agent must learn when the user prefers each modality. Information about these user preferences are kept in the user interaction profile, namely situations when the user: requires a suggestion to deal with a problem, needs only a warning about a problem, accepts an interruption from the agent, expects an action on his or her 2.6 Individual Characteristics In some domains, personal information about the user is also part of the user pro file. This item includes mainly demographic information such as gender, age marital status, city, country, number of children, among other features. For exam- ple, Figure 3 shows the demographic profile of a customer in Traveller, a tourism recommender system that recommends package holidays and tours to customers On the other hand, a widely used user characteristic in intelligent tutoring sys- tems and adaptive e-learning systems is the students learning style. A learning style model classifies students according to where they fit in a number of scales belonging to the ways in which they receive and process information. There have been proposed several models and frameworks for learning styles(Kolb 1984 Felder and Silverman, 1988; Honey and Mumford, 1992, Litzinger and Osif, 1993). For example, Felder and Silverman's model categorizes students as sensi- tive/intuitive, visual/verbal, active/reflective, and sequential/global, depending on how they learn. Various systems consider learning styles, such as ARTHUR (GilbertIntelligent User Profiling 199 and the moment when each query is generally made. The agent detects hourly, daily, weekly, and monthly behavioural patterns. 2.5 Interaction Preferences A quite new component of a user profile is interaction preferences, that is, infor￾mation about the user’s interaction habits and preferences when he interacts with an interface agent (Schiaffino and Amandi, 2006). In interface agent technology, it is vital to know which agent’s actions the user expects in different contexts and the modality of these actions. A user may prefer warnings, suggestions, or actions on the user’s behalf. In addition, the agent can provide assistance by interrupting or not interrupting the user’s work. A user interaction preference then expresses the preferred agent action and modality for different situations or contexts. As an illustration, consider an agent helping a user, John Smith, organize his calendar. Smith’s current task is to schedule a meeting with several participants for the following Saturday in a free time slot. From past experience, the agent knows that one participant will disagree with the meeting date, because he never attends Sat￾urday meetings. The agent can: warn the user about this problem, suggest another meeting date that considers all participant preferences and priorities, or do noth￾ing. In this situation, some users would prefer a simple warning, while others would want suggestions about an alternative meeting date. In addition, when pro￾viding user assistance, agents can either interrupt the user’s work or not. The agent must learn when the user prefers each modality. Information about these user preferences are kept in the user interaction profile, namely situations when the user: requires a suggestion to deal with a problem, needs only a warning about a problem, accepts an interruption from the agent, expects an action on his or her behalf, and wants a notification rather than an interruption. 2.6 Individual Characteristics In some domains, personal information about the user is also part of the user pro￾file. This item includes mainly demographic information such as gender, age, marital status, city, country, number of children, among other features. For exam￾ple, Figure 3 shows the demographic profile of a customer in Traveller, a tourism recommender system that recommends package holidays and tours to customers. On the other hand, a widely used user characteristic in intelligent tutoring sys￾tems and adaptive e-learning systems is the student’s learning style. A learning￾style model classifies students according to where they fit in a number of scales belonging to the ways in which they receive and process information. There have been proposed several models and frameworks for learning styles (Kolb 1984; Felder and Silverman, 1988; Honey and Mumford, 1992; Litzinger and Osif, 1993). For example, Felder and Silverman’s model categorizes students as sensi￾tive/intuitive, visual/verbal, active/reflective, and sequential/global, depending on how they learn. Various systems consider learning styles, such as ARTHUR (Gilbert