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    A Graph-based Recommender System for Digital Library

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    Author
    Huang, Zan
    Chung, Wingyan
    Ong, Thian-Huat
    Chen, Hsinchun
    Issue Date
    2002
    Submitted date
    2004-08-20
    Keywords
    Evaluation
    Digital Libraries
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Recommender system
    Hopfield net algorithm
    Graph-based model
    Content-based filtering
    Collaborative
    Filtering
    Mutual information algorithm
    Chinese phrase
    Extraction
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    Citation
    A Graph-based Recommender System for Digital Library 2002, :65-73
    Publisher
    ACM/IEEE-CS
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105313
    Abstract
    Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, useruser and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.
    Type
    Conference Paper
    Language
    en
    Collections
    DLIST

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