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ACM Computing Surveys (CSUR)
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Explaining collaborative filtering recommendations
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From adaptive hypertext to personalized web companions
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IEEE Internet Computing
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CHI '01 Extended Abstracts on Human Factors in Computing Systems
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
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Revised Papers from the nternational Workshops OHS-7, SC-3, and AH-3 on Hypermedia: Openness, Structural Awareness, and Adaptivity
Usable adaptive hypermedia systems
Hypermedia - Special issue: Adaptive hypermedia in the age of the adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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The impact of learner attributes and learner choice in an agent-based environment
Computers & Education
Journal of the American Society for Information Science and Technology
Information Processing and Management: an International Journal
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Traditional hypermedia applications present the same content and provide identical navigational support to all users. Adaptive Hypermedia Systems (AHS) make it possible to construct personalized presentations to each user, according to preferences and needs identified. We present in this paper an alternative approach to educational AHS where a virtual character personalizes the interaction with the user through the use of a particular recommender system. The character has natural language communication abilities; it can learn students' profiles and use this knowledge to recommend appropriate contents and activities. Through its interaction with the user, the character is able to collect and organize information about students in order to identify appropriate suggestions of contents. The recommender system employs a knowledge representation scheme that is easy to understand and to modify, enabling teachers/tutors to explore the types of recommendations being made and to appreciate why they are made. An experiment with computer science students was carried out in order to validate the approach proposed. The results of the experiment showed that the presentation of personalized links through a virtual character had a positive impact in the users' perception of the system as a learning tool. The combination of the virtual character with a recommender system proved to be a good alternative for the delivery of personalized contents without making constant changes in the main user interface. This approach provides mechanisms to guide users through paths of study followed by students with similar profiles, without violating the human-computer interaction principle of perceived stability.