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A Recommender System for Learning Objects Personalized Retrieval

A Recommender System for Learning Objects Personalized Retrieval

Ana Casali, Valeria Gerling, Claudia Deco, Cristina Bender
Copyright: © 2012 |Pages: 29
ISBN13: 9781613504895|ISBN10: 1613504896|EISBN13: 9781613504901
DOI: 10.4018/978-1-61350-489-5.ch008
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MLA

Olga C. Santos and Jesus G. Boticario. "A Recommender System for Learning Objects Personalized Retrieval." Educational Recommender Systems and Technologies: Practices and Challenges, IGI Global, 2012, pp.182-210. https://doi.org/10.4018/978-1-61350-489-5.ch008

APA

O. Santos & J. Boticario (2012). A Recommender System for Learning Objects Personalized Retrieval. IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch008

Chicago

Olga C. Santos and Jesus G. Boticario. "A Recommender System for Learning Objects Personalized Retrieval." In Educational Recommender Systems and Technologies: Practices and Challenges. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-489-5.ch008

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Abstract

This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.

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