Class: JrubyMahout::RecommenderBuilder

Inherits:
Object
  • Object
show all
Defined in:
lib/jruby_mahout/recommender_builder.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(similarity_name, neighborhood_size, recommender_name, is_weighted) ⇒ RecommenderBuilder

public interface RecommenderBuilder Implementations of this inner interface are simple helper classes which create a Recommender to be evaluated based on the given DataModel.



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# File 'lib/jruby_mahout/recommender_builder.rb', line 23

def initialize(similarity_name, neighborhood_size, recommender_name, is_weighted)
  @is_weighted = is_weighted
  @neighborhood_size = neighborhood_size
  @similarity_name = similarity_name
  @recommender_name = recommender_name
  @item_based_allowed = (@similarity_name == "SpearmanCorrelationSimilarity") ? false : true
end

Instance Attribute Details

#item_based_allowed ⇒ Object

Returns the value of attribute item_based_allowed.



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# File 'lib/jruby_mahout/recommender_builder.rb', line 20

def item_based_allowed
  @item_based_allowed
end

#recommender_name ⇒ Object

Returns the value of attribute recommender_name.



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# File 'lib/jruby_mahout/recommender_builder.rb', line 20

def recommender_name
  @recommender_name
end

Instance Method Details

#buildRecommender(data_model) ⇒ Object

buildRecommender(DataModel dataModel) Builds a Recommender implementation to be evaluated, using the given DataModel.



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# File 'lib/jruby_mahout/recommender_builder.rb', line 33

def buildRecommender(data_model)
  begin
    case @similarity_name
      when "PearsonCorrelationSimilarity"
        similarity = (@is_weighted) ? PearsonCorrelationSimilarity.new(data_model, Weighting::WEIGHTED) : PearsonCorrelationSimilarity.new(data_model)
      when "EuclideanDistanceSimilarity"
        similarity = (@is_weighted) ? EuclideanDistanceSimilarity.new(data_model, Weighting::WEIGHTED) : EuclideanDistanceSimilarity.new(data_model)
      when "SpearmanCorrelationSimilarity"
        similarity = SpearmanCorrelationSimilarity.new(data_model)
      when "LogLikelihoodSimilarity"
        similarity = LogLikelihoodSimilarity.new(data_model)
      when "TanimotoCoefficientSimilarity"
        similarity = TanimotoCoefficientSimilarity.new(data_model)
      when "GenericItemSimilarity"
        similarity = PearsonCorrelationSimilarity.new(data_model, Weighting::WEIGHTED)
      else
        similarity = nil
    end

    unless @neighborhood_size.nil?
      if @neighborhood_size > 1
        neighborhood = NearestNUserNeighborhood.new(Integer(@neighborhood_size), similarity, data_model)
      elsif @neighborhood_size >= -1 and @neighborhood_size <= 1
        neighborhood = ThresholdUserNeighborhood.new(Float(@neighborhood_size), similarity, data_model)
      end
    end

    case @recommender_name
      when "GenericUserBasedRecommender"
        recommender = GenericUserBasedRecommender.new(data_model, neighborhood, similarity)
      when "GenericItemBasedRecommender"
        recommender = (@item_based_allowed) ? GenericItemBasedRecommender.new(data_model, similarity) : nil
      when "SlopeOneRecommender"
        recommender = SlopeOneRecommender.new(data_model)
      else
        recommender = nil
    end

    recommender
  rescue Exception => e
    return e
  end
end