Example usage for org.apache.mahout.cf.taste.similarity UserSimilarity setPreferenceInferrer

Introduction

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

Prototype

void setPreferenceInferrer(PreferenceInferrer inferrer);

Source Link

Document

Attaches a PreferenceInferrer to the UserSimilarity implementation.

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  ww  w.  ja  v a2  s  .  co  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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