《电子商务 E-business》阅读文献：intelligent user profiling

198 S Schiaffino and A. Amandi hence, the user's goal. Similarly, the Lumiere project at Microsoft Research( Hor- vitz et al., 1998)uses Bayesian networks to infer a users needs by considering a user's background, actions and queries(help requests). Based on the beliefs of a user's needs and the utility theory of influence diagrams(an extension to Bayesian networks), an automated assistant provides help for users. In Andes( gertner and VanLehn, 2000), plan recognition is necessary for the problem solving coach select what step to suggest when a student asks for help. Since Andes wants to help students solve problems in their own way, it must determine what goal the student is probably trying to achieve, and suggest the action the student cannot perform due to lack of knowledge 2.4 Behaviour Usually, the user's behaviour with a software application is an important part of the user profile. If a given user behaviour is repetitive, then it represents a pattern that can be used by an adaptive system or an intelligent agent to adapt a web site or to assist the user according to the behaviour learnt. The type of behaviour mod- elled depends on the application domain. For example, CAP (Calendar APpren- tice)learns the scheduling behaviour of its user and learns rules that enable it to suggest the meeting duration, location, time, and date(Mitchell et al, 1994 ) In an intelligent e-commerce system, a behavioural profile models the customer's ac tions(Adomavicius and Tuzhilin, 2001). Examples of behaviours in this domain e"When purchasing cereal, John Doe usually buys milk"and"On weekend John Doe usually spends more than $100 on groceries". In intelligent tutoring systems, the student behaviour is vital to assist him properly. In(Xu, 2002),a student profile is a set of <t, e> pairs, where e is a behaviour of the student and t expresses the time when the behaviour occurs. t could be a point in time or interval of time. In this work, there are two main types of student behaviours and making a choice in a quiz. ometimes behaviours are routine, that is, they show some kind of regularity or seasonality. For example, Query Guesser(Schiaffino and Amandi, 2005) models a users routine queries to a database in a Laboratory Information Management System. In this agent, the user profile is composed of the queries each user performs F如25.9 Fae18.39 plectcontact ComposeMailTod MIrs. 333 Fig. 2. Bayesian representation of a user's goal

198 S. Schiaffino and A. Amandi hence, the user’s goal. Similarly, the Lumiere project at Microsoft Research (Hor￾vitz et al., 1998) uses Bayesian networks to infer a user’s needs by considering a user’s background, actions and queries (help requests). Based on the beliefs of a user’s needs and the utility theory of influence diagrams (an extension to Bayesian networks), an automated assistant provides help for users. In Andes (Gertner and VanLehn, 2000), plan recognition is necessary for the problem solving coach to select what step to suggest when a student asks for help. Since Andes wants to help students solve problems in their own way, it must determine what goal the student is probably trying to achieve, and suggest the action the student cannot perform due to lack of knowledge. 2.4 Behaviour Usually, the user’s behaviour with a software application is an important part of the user profile. If a given user behaviour is repetitive, then it represents a pattern that can be used by an adaptive system or an intelligent agent to adapt a web site or to assist the user according to the behaviour learnt. The type of behaviour mod￾elled depends on the application domain. For example, CAP (Calendar APpren￾tice) learns the scheduling behaviour of its user and learns rules that enable it to suggest the meeting duration, location, time, and date (Mitchell et al, 1994). In an intelligent e-commerce system, a behavioural profile models the customer’s ac￾tions (Adomavicius and Tuzhilin, 2001). Examples of behaviours in this domain are “When purchasing cereal, John Doe usually buys milk” and “On weekends, John Doe usually spends more than $100 on groceries”. In intelligent tutoring systems, the student behaviour is vital to assist him properly. In (Xu, 2002), a student profile is a set of <t, e> pairs, where e is a behaviour of the student and t expresses the time when the behaviour occurs. t could be a point in time or an interval of time. In this work, there are two main types of student behaviours, reading a particular topic and making a choice in a quiz. Sometimes behaviours are routine, that is, they show some kind of regularity or seasonality. For example, QueryGuesser (Schiaffino and Amandi, 2005) models a user’s routine queries to a database in a Laboratory Information Management System. In this agent, the user profile is composed of the queries each user performs Fig. 2. Bayesian representation of a user’s goals

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 (Gilbert

Intelligent 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

200 S Schiaffino and A Amandi nide\Peril Colby atso Fig 3. Demographic profile of a customer in Traveller and Han, 1999) which models three learning styles(visual-interactive, reading listener, textual), CS388( Carver et al, 1996) and MAs-PLANG(Pena et al. 2002 that use Felder and Silverman styles; the INSPIRE system( Grigoriadou et al 2001)that uses the styles proposed by Honey and Mumford Finally, personality traits are also important features in a user profile. a trait is a temporally stable, cross-situational individual difference. One of the most fa mous personality models is OCEAN(Goldberg, 1993). This model comprises five personality dimensions: Openness to Experience, Conscientiousness, Extraver detengreeableness, and Neuroticism. Personality models and the methods to ne personality are subjects widely studied in psychology(McCrae and Costa, 1996; Wiggins et al, 1988). In the area of user profiling, various methods e used to detect user's personality. For example, in(Arya et al, 2006)facial actions are used as visual cues for detecting personality 2.7 Contextual Information The user's context is a quite new feature in user profiling. There are several defi- nitions of context, mostly depending on the application domain. According to Dey and Abwod, 1999), context is any information that can be used to character- ize the situation of an entity. An entity is a person, place, or object that is consid- ered relevant to the interaction between a user and an application, including the user and applications themselves. There are different types of contexts or contex- tual information that can be modelled within a user profile, as defined in( Goker and Myrhaug, 2002). The environmental context captures the entities that sur- round the user. These entities can, for instance, be things, services, temperature light, humidity, noise, and persons. The personal context includes the physiologi- cal context and the mental context. The first part can contain information like pulse, blood pressure, weight, glucose level, retinal pattern, and hair colour. The latter part can contain information like mood, expertise, angriness, and stress. The social context describes the social aspects of the current user context. It can con-

200 S. Schiaffino and A. Amandi Fig. 3. Demographic profile of a customer in Traveller and Han, 1999) which models three learning styles (visual-interactive, reading￾listener, textual), CS388 (Carver et al, 1996) and MAS-PLANG (Peña et al., 2002) that use Felder and Silverman styles; the INSPIRE system (Grigoriadou et al., 2001) that uses the styles proposed by Honey and Mumford. Finally, personality traits are also important features in a user profile. A trait is a temporally stable, cross-situational individual difference. One of the most fa￾mous personality models is OCEAN (Goldberg, 1993). This model comprises five personality dimensions: Openness to Experience, Conscientiousness, Extraver￾sion, Agreeableness, and Neuroticism. Personality models and the methods to determine personality are subjects widely studied in psychology (McCrae and Costa, 1996; Wiggins et al, 1988). In the area of user profiling, various methods are used to detect user’s personality. For example, in (Arya et al, 2006) facial actions are used as visual cues for detecting personality. 2.7 Contextual Information The user’s context is a quite new feature in user profiling. There are several defi￾nitions of context, mostly depending on the application domain. According to (Dey and Abwod, 1999), context is any information that can be used to character￾ize the situation of an entity. An entity is a person, place, or object that is consid￾ered relevant to the interaction between a user and an application, including the user and applications themselves. There are different types of contexts or contex￾tual information that can be modelled within a user profile, as defined in (Goker and Myrhaug, 2002). The environmental context captures the entities that sur￾round the user. These entities can, for instance, be things, services, temperature, light, humidity, noise, and persons. The personal context includes the physiologi￾cal context and the mental context. The first part can contain information like pulse, blood pressure, weight, glucose level, retinal pattern, and hair colour. The latter part can contain information like mood, expertise, angriness, and stress. The social context describes the social aspects of the current user context. It can con-