SVDPlusPlus

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. final def eq(arg0: AnyRef): Boolean

   

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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. final def getClass(): Class[_]

   

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11. final def isInstanceOf[T0]: Boolean

    

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12. final def ne(arg0: AnyRef): Boolean

    

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13. final def notify(): Unit

    

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14. final def notifyAll(): Unit

    

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

    

    Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", available at here.

    Implement SVD++ based on "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", available at here.

    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

16. final def synchronized[T0](arg0: ⇒ T0): T0

    

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17. def toString(): String

    

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18. final def wait(): Unit

    

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19. final def wait(arg0: Long, arg1: Int): Unit

    

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20. final def wait(arg0: Long): Unit

    

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