ntelligent User Profiling software agent records the interactions between the user and a conventional inter- face, and writes a program that corresponds to the users actions. The agent can then generalize the program so that it can work in other situations similar to, but not necessarily exactly the same as, the which it is taught. for exam- ple, in(Ruvini and Dony, 2001)a software agent detects habitual patterns in a conventional programming language environment, Smalltalk, and automates those 3.2 Observ ation of a User's actions There are various problems with explicit user information. First, users are gener ally not willing to provide information by filling in long forms. Second, they not always tell or write the truth when completing forms about themselves. Third although some of them might be willing to provide data, they sometimes do not know how to express their interests or what they really want. Thus, the most widely used method for obtaining information about users is observing their ac tions with the underlying application, recording or logging these actions, and dis- covering patterns from these logs through some Machine Learning or Data Mining order to learn a user profile from a user's actions, there are certain conditions that must be fulfilled. The user behaviour has to be repetitive, that is the same actions have to be performed under similar conditions in different time points. If there is no repetition, no pattern can be discovered. In addition, the behaviour observed has to be different for different users. If not, there is no need for building an individual user profile For example, PersonalSearcher( Godoy et al, 2004)unobtrusively observes a users browsing behaviour in order to approximate the degree of user interest each visited web page. In order to accomplish this goal, for each read page in a standard browser the agent observes a set of implicit indicators in a process known as implicit feedback(Oard and Kim, 1998). Implicit interest indicators used by Personal Searcher include the time consumed in reading a web page( considering its length), the amount of scrolling in a page, and whether it was added to the list of bookmarks or not. Similarly, NewsAgent monitors users' behaviour while they are reading newspapers on the web and it records information about the different articles they read and some indicators about their relevance to the user a key characteristic of learning through observation is that of adapting to the users changing interests, preferences, habits and goals. The user profiling tech- niques used have to be able to adapt the content of the user profile as new observa- tions are recorded. User feedback plays a fundamental role in this task, as ex plained in the next section.Intelligent User Profiling 203 software agent records the interactions between the user and a conventional inter￾face, and writes a program that corresponds to the user’s actions. The agent can then generalize the program so that it can work in other situations similar to, but not necessarily exactly the same as, the examples on which it is taught. For exam￾ple, in (Ruvini and Dony, 2001) a software agent detects habitual patterns in a conventional programming language environment, Smalltalk, and automates those patterns. 3.2 Observation of a User’s Actions There are various problems with explicit user information. First, users are gener￾ally not willing to provide information by filling in long forms. Second, they not always tell or write the truth when completing forms about themselves. Third, although some of them might be willing to provide data, they sometimes do not know how to express their interests or what they really want. Thus, the most widely used method for obtaining information about users is observing their ac￾tions with the underlying application, recording or logging these actions, and dis￾covering patterns from these logs through some Machine Learning or Data Mining technique. In order to learn a user profile from a user’s actions, there are certain conditions that must be fulfilled. The user behaviour has to be repetitive, that is the same actions have to be performed under similar conditions in different time points. If there is no repetition, no pattern can be discovered. In addition, the behaviour observed has to be different for different users. If not, there is no need for building an individual user profile. For example, PersonalSearcher (Godoy et al, 2004) unobtrusively observes a user’s browsing behaviour in order to approximate the degree of user interest in each visited web page. In order to accomplish this goal, for each read page in a standard browser the agent observes a set of implicit indicators in a process known as implicit feedback (Oard and Kim, 1998). Implicit interest indicators used by Personal Searcher include the time consumed in reading a web page (considering its length), the amount of scrolling in a page, and whether it was added to the list of bookmarks or not. Similarly, NewsAgent monitors users’ behaviour while they are reading newspapers on the web and it records information about the different articles they read and some indicators about their relevance to the user. A key characteristic of learning through observation is that of adapting to the user’s changing interests, preferences, habits and goals. The user profiling tech￾niques used have to be able to adapt the content of the user profile as new observa￾tions are recorded. User feedback plays a fundamental role in this task, as ex￾plained in the next section