ntelligent User profi 195 2.1 Interests User interests are one of the most important (and typically the only) part of the user profile in information retrieval and filtering systems, recommender systems, some interface agents, and adaptive systems that are information-driven such as encyclopedias, museum guides, and news systems(Brusilovsky and Millan 2007). Interests can represent news topics, web page topics, document topics, work-related topics or hobbies-related topics. Sometimes user interests are classi fied as short-term interests or long-term interests. The interest of users in football may be a short-term interest if the user reads or listens to news about this topic only during the World Cup, or a long-term interest if the user is always interested in this topic. For example, Newsdude(Billsus and Pazzani, 1999), an interface agent that learns about a users interests in daily news stories, considers informa- tion about recent events as short-term interests, and a users general preferences for news stories as long-term interests The most common representation of user interests are keyword-based models In these models interests are represented by weighted vectors of keywor Weights traditionally represent the relevance of the word for the user or within the topic. These representations are common in the Information Filtering and Informa- tion Retrieval areas. For example Letizia (lieberman et al, 2001a), a browsing assistant,uses TF-IDF (term frequency/inverse document frequency) vectors to model user interests. In this technique the weight of each word is calculated by comparing the word frequency in a document against the word frequency in all the documents in a corpus(Salton and McGill, 1983). This technique is also used NewsDude(Billsus and Pazzani, 1999), where news stories are converted to tF IDF vectors A more powerful representation of user interests is through topic hierarchies Godoy et al, 2004). Each node in the hierarchy represents a topic of interest for a user, which is defined by a set of representative words. This representation tech- nique is important when we want to model not only general user interests such as of these interests that are relevant to a given user. For example, the user profile can indicate that a certain user is inter- ested in documents talking about a famous football player and not in sports or football in general. An example of a topic hierarchy containing a users interests is shown in Figure I Often, a topic ontology is used as the reference to construct a user interest pro- file. An ontology is a conceptualization of a domain into a human-understandable, but machine-readable format consisting of entities, attributes, relationships, and axioms( Guarino and Giaretta 1995). For instance, in Quickstep(middleton et al 2004), the authors represent user profiles in terms of a research paper topic onto laboratory setting, representing user profiling with a research topic ontology ogy. This recommender system was built to help researchers in a computer sciend using ontological inference to assist the profiling process. Similarly, in(Liang et al, 2007)students' interests within an e-learning system are determined using a topic ontologyIntelligent User Profiling 195 2.1 Interests User interests are one of the most important (and typically the only) part of the user profile in information retrieval and filtering systems, recommender systems, some interface agents, and adaptive systems that are information-driven such as encyclopedias, museum guides, and news systems (Brusilovsky and Millán, 2007). Interests can represent news topics, web page topics, document topics, work-related topics or hobbies-related topics. Sometimes user interests are classi￾fied as short-term interests or long-term interests. The interest of users in football may be a short-term interest if the user reads or listens to news about this topic only during the World Cup, or a long-term interest if the user is always interested in this topic. For example, NewsDude (Billsus and Pazzani, 1999), an interface agent that learns about a user’s interests in daily news stories, considers informa￾tion about recent events as short-term interests, and a user’s general preferences for news stories as long-term interests. The most common representation of user interests are keyword-based models. In these models interests are represented by weighted vectors of keywords. Weights traditionally represent the relevance of the word for the user or within the topic. These representations are common in the Information Filtering and Informa￾tion Retrieval areas. For example Letizia (Lieberman et al, 2001a), a browsing assistant, uses TF-IDF (term frequency/inverse document frequency) vectors to model user interests. In this technique the weight of each word is calculated by comparing the word frequency in a document against the word frequency in all the documents in a corpus (Salton and McGill, 1983). This technique is also used in NewsDude (Billsus and Pazzani, 1999), where news stories are converted to TF￾IDF vectors. A more powerful representation of user interests is through topic hierarchies (Godoy et al, 2004). Each node in the hierarchy represents a topic of interest for a user, which is defined by a set of representative words. This representation tech￾nique is important when we want to model not only general user interests such as sports or economy, but also the sub-topics of these interests that are relevant to a given user. For example, the user profile can indicate that a certain user is inter￾ested in documents talking about a famous football player and not in sports or football in general. An example of a topic hierarchy containing a user’s interests is shown in Figure 1. Often, a topic ontology is used as the reference to construct a user interest pro￾file. An ontology is a conceptualization of a domain into a human-understandable, but machine-readable format consisting of entities, attributes, relationships, and axioms (Guarino and Giaretta 1995). For instance, in Quickstep (Middleton et al, 2004), the authors represent user profiles in terms of a research paper topic ontol￾ogy. This recommender system was built to help researchers in a computer science laboratory setting, representing user profiling with a research topic ontology and using ontological inference to assist the profiling process. Similarly, in (Liang et al, 2007) students’ interests within an e-learning system are determined using a topic ontology