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COBRAS: Cooperative CBR Bibliographic Recommender System

COBRAS: Cooperative CBR Bibliographic Recommender System

Hager Karoui
Copyright: © 2009 |Pages: 19
ISBN13: 9781605663067|ISBN10: 1605663069|ISBN13 Softcover: 9781616924829|EISBN13: 9781605663074
DOI: 10.4018/978-1-60566-306-7.ch009
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MLA

Karoui, Hager. "COBRAS: Cooperative CBR Bibliographic Recommender System." Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, et al., IGI Global, 2009, pp. 184-202. https://doi.org/10.4018/978-1-60566-306-7.ch009

APA

Karoui, H. (2009). COBRAS: Cooperative CBR Bibliographic Recommender System. In M. Chevalier, C. Julien, & C. Soule-Dupuy (Eds.), Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling (pp. 184-202). IGI Global. https://doi.org/10.4018/978-1-60566-306-7.ch009

Chicago

Karoui, Hager. "COBRAS: Cooperative CBR Bibliographic Recommender System." In Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, edited by Max Chevalier, Christine Julien, and Chantal Soule-Dupuy, 184-202. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-306-7.ch009

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Abstract

In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference recommender system. COBRAS’s task is to find relevant documents and interesting people related to the interests and preferences of a single person belonging to a like-minded group in an implicit and an intelligent way. Each user manages their own bibliographical database in isolation from others. Target users use a common vocabulary for document indexing but may interpret the indexing vocabulary differently from others. Software agents are used to ensure indirect cooperation between users. A P2P architecture is used to allow users to control their data sharing scheme with others and to ensure their autonomy and privacy. The system associates a software assistant agent with each user. Agents are attributed three main skills: a) detecting the associated user’s hot topics, b) selecting a subset of peer agents that are likely to provide relevant recommendations, and c) recommending both documents and other agents in response to a recommendation request sent by a peer agent. The last two skills are handled by implementing two inter-related data-driven case-based reasoning systems. The basic idea underlying the document recommendation process is to map hot topics sent by an agent to local topics. Documents indexed by mapped topics are then recommended to the requesting agent. This agent will provide later, a relevance feedback computed after the user evaluation of the received recommendations. Provided feedbacks are used to learn to associate a community of peer agents to each local hot topic. An experimental study involving one hundred software agents using real bibliographical data is described. The Obtained results demonstrate the validity of the proposed approach.

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