Package org.apache.mahout.cf.taste.eval

Examples of org.apache.mahout.cf.taste.eval.RecommenderBuilder

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    DataModel model = new FileDataModel(new File("ratings.dat"));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();

    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        try {
          return new LibimsetiRecommender(model);
        } catch (IOException ioe) {
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  public static void main(String[] args) throws Exception {
    DataModel model = new FileDataModel(new File("ratings.dat"));
    model = new GenericBooleanPrefDataModel(GenericBooleanPrefDataModel.toDataMap(model));
      RecommenderIRStatsEvaluator evaluator =
        new GenericRecommenderIRStatsEvaluator();
      RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
          UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
          UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
          return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
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  public static void main(String[] args) throws Exception {
    DataModel model = new GroupLensDataModel(new File("ratings.dat"));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(100, similarity, model);
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        GenericBooleanPrefDataModel.toDataMap(
          new FileDataModel(new File("ua.base"))));

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
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    DataModel model = new FileDataModel(modelFile);

    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
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    DataModel model = new FileDataModel(modelFile);

    RecommenderIRStatsEvaluator evaluator =
      new GenericRecommenderIRStatsEvaluator();
    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
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        GenericBooleanPrefDataModel.toDataMap(
          new FileDataModel(new File("ua.base"))));

    RecommenderIRStatsEvaluator evaluator =
      new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new LogLikelihoodSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
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public final class RMSRecommenderEvaluatorTest extends TasteTestCase {

  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
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public final class AverageAbsoluteDifferenceRecommenderEvaluatorTest extends TasteTestCase {

  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
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public final class GenericRecommenderIRStatsEvaluatorImplTest extends TasteTestCase {

  public void testEvaluate() throws Exception {
    DataModel model = getDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) throws TasteException {
        return new SlopeOneRecommender(dataModel);
      }
    };
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Related Classes of org.apache.mahout.cf.taste.eval.RecommenderBuilder

  • com.manning.hip.ch9.MovieUserEvaluator
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch02.ItemBaseRecommenderEvaluation
  • mia.recommender.ch02.UserBaseRecommenderEvaluation
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch05.LibimsetiEvalRunner
  • mia.recommender.ch05.LibimsetiIREvalRunner
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