SVDPlusPlus

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17. def run(edges: RDD[Edge[Double]], conf: Conf): (Graph[(DoubleMatrix, DoubleMatrix, Double, Double), Double], Double)

    Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", available at http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf.

    Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", available at http://public.research.att.com/~volinsky/netflix/kdd08koren.pdf.

    The prediction rule is rui = u + bu + bi + qi*(pu + |N(u)|^(-0.5)*sum(y)), see the details on page 6.

    edges

    edges for constructing the graph

    conf

    SVDPlusPlus parameters

    returns

    a graph with vertex attributes containing the trained model

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