Example usage for org.apache.mahout.cf.taste.impl.similarity AveragingPreferenceInferrer AveragingPreferenceInferrer

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.similarity AveragingPreferenceInferrer AveragingPreferenceInferrer.

Prototype

public AveragingPreferenceInferrer(DataModel dataModel) throws TasteException 

Source Link

Usage

From source file:cf.wikipedia.WikipediaTasteUserDemo.java

License:Apache License

public static void main(String[] args)
        throws IOException, TasteException, SAXException, ParserConfigurationException {
    String recsFile = args[0];/*from w w  w .ja  va 2  s. c o m*/
    String docIdsTitle = args[1];
    Integer neighborhoodSize = Integer.parseInt(args[2]);
    Long userId = Long.parseLong(args[3]);
    boolean printCommonalities = Boolean.parseBoolean(args[4]);
    InputSource is = new InputSource(new FileInputStream(docIdsTitle));
    SAXParserFactory factory = SAXParserFactory.newInstance();
    factory.setValidating(false);
    SAXParser sp = factory.newSAXParser();
    WikiContentHandler handler = new WikiContentHandler();
    sp.parse(is, handler);
    //create the data model
    FileDataModel dataModel = new FileDataModel(new File(recsFile));
    System.out.println("Data Model: Users: " + dataModel.getNumUsers() + " Items: " + dataModel.getNumItems());

    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel);
    // Optional:
    userSimilarity.setPreferenceInferrer(new AveragingPreferenceInferrer(dataModel));
    //Get a neighborhood of users
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(neighborhoodSize, userSimilarity, dataModel);
    //Create the recommender
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, userSimilarity);
    System.out.println("-----");
    System.out.println("User: " + userId);
    //Print out the users own preferences first
    TasteUtils.printPreferences(dataModel, userId, handler.map);
    if (printCommonalities) {
        long[] users = neighborhood.getUserNeighborhood(userId);
        for (int i = 0; i < users.length; i++) {
            long neighbor = users[i];
            System.out.println("Neighbor: " + neighbor);
            TasteUtils.printCommonalities(dataModel, userId, neighbor, handler.map);
        }

        System.out.println("");
    }
    //Get the top 5 recommendations
    List<RecommendedItem> recommendations = recommender.recommend(userId, 5);
    TasteUtils.printRecs(recommendations, handler.map);
}

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