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  • User Graph Regularized Pairwise Matrix Factorization for Item Recommendation

    Author(s)
    Du, Liang
    Li, Xuan
    Shen, Yi-Dong
    Griffith University Author(s)
    Shen, Yi-Dong
    Year published
    2011
    Metadata
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    Abstract
    Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing ...
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    Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task.
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    Journal Title
    Lecture Notes in Computer science
    Volume
    7121
    DOI
    https://doi.org/10.1007/978-3-642-25856-5_28
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/46984
    Collection
    • Journal articles

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