object 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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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
-
final
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toString(): String
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