Example usage for org.apache.mahout.cf.taste.impl.recommender GenericItemBasedRecommender mostSimilarItems

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.recommender GenericItemBasedRecommender mostSimilarItems.

Prototype

@Override
    public List<RecommendedItem> mostSimilarItems(long[] itemIDs, int howMany) throws TasteException 

Source Link

Usage

From source file:businessreco.BusinessReco.java

public static void main(String args[]) {

    try {/*from ww  w. j  av a2s.c  o  m*/

        //Loading the DATA;    

        DataModel dm = new FileDataModel(new File(
                "C:\\Users\\bryce\\Course Work\\3. Full Summer\\Big Data\\Final Project\\Yelp\\FINAL CODE\\Mahout\\data\\busirec_new.csv"));

        // We use the below line to relate businesses. 
        //ItemSimilarity sim = new LogLikelihoodSimilarity(dm);

        TanimotoCoefficientSimilarity sim = new TanimotoCoefficientSimilarity((dm));

        //Using the below line get recommendations
        GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(dm, sim);

        //Looping through every business.
        for (LongPrimitiveIterator items = dm.getItemIDs(); items.hasNext();) {
            long itemId = items.nextLong();

            // For each business we recommend 3 businesses.

            List<RecommendedItem> recommendations = recommender.mostSimilarItems(itemId, 2);

            for (RecommendedItem recommendation : recommendations) {

                System.out.println(itemId + "," + recommendation.getItemID() + "," + recommendation.getValue());

            }

        }
    }

    catch (IOException | TasteException e) {
        System.out.println(e);
    }

}

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