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package root

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root
package org

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root
package apache

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org
package spark
Core Spark functionality.
Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.
In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.
Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.
Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes
apache
package graphx
ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.
ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.

Definition Classes
spark
package lib
Various analytics functions for graphs.
Various analytics functions for graphs.

Definition Classes
graphx
ConnectedComponents
LabelPropagation
PageRank
SVDPlusPlus
ShortestPaths
StronglyConnectedComponents
TriangleCount

org.apache.spark.graphx.lib

SVDPlusPlus

object SVDPlusPlus

Implementation of SVD++ algorithm.

Source: SVDPlusPlus.scala

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class Conf extends Serializable
Configuration parameters for SVDPlusPlus.

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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
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SVDPlusPlus

object SVDPlusPlus

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SVDPlusPlus 

object SVDPlusPlus

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SVDPlusPlus