Examples with Preference org.apache.mahout.cf.taste.model.Preference used on opensource projects

Example 1 with Preference

use of org.apache.mahout.cf.taste.model.Preference in project facebook-recommender-demo by ManuelB.

the class AnimalFoodRecommender method initDataModel.

public void initDataModel() {
    FastByIDMap<PreferenceArray> preferenceMap = new FastByIDMap<PreferenceArray>();
    for (int i = 0; i < animals.size(); i++) {
        List<Preference> userPreferences = new ArrayList<Preference>();
        long userId = id2thing.toLongID(animals.get(i));
        for (int j = 0; j < foods.size(); j++) {
            if (preferences[i][j] != null) {
                userPreferences.add(new GenericPreference(userId, id2thing.toLongID(foods.get(j)), preferences[i][j]));
            }
        }
        GenericUserPreferenceArray userArray = new GenericUserPreferenceArray(userPreferences);
        preferenceMap.put(userId, userArray);
    }
    model = new GenericDataModel(preferenceMap);
}

Example 2 with Preference

use of org.apache.mahout.cf.taste.model.Preference in project facebook-recommender-demo by ManuelB.

the class FacebookRecommender method initRecommender.

/**
	 * This function will init the recommender
	 * it will load the CSV file from the resource folder,
	 * parse it and create the necessary data structures
	 * to create a recommender.
	 * The 
	 */
@PostConstruct
public void initRecommender() {
    try {
        // get the file which is part of the WAR as
        URL url = getClass().getClassLoader().getResource(DATA_FILE_NAME);
        // create a file out of the resource
        File data = new File(url.toURI());
        // create a map for saving the preferences (likes) for
        // a certain person
        Map<Long, List<Preference>> preferecesOfUsers = new HashMap<Long, List<Preference>>();
        // use a CSV parser for reading the file
        // use UTF-8 as character set
        CSVParser parser = new CSVParser(new InputStreamReader(new FileInputStream(data), "UTF-8"));
        // parse out the header
        // we are not using the header
        String[] header = parser.getLine();
        // should output person name
        log.fine(header[0] + " " + header[1]);
        String[] line;
        // go through every line
        while ((line = parser.getLine()) != null) {
            String person = line[0];
            String likeName = line[1];
            // other lines contained but not used
            // String category = line[2];
            // String id = line[3];
            // String created_time = line[4];
            // create a long from the person name
            long userLong = thing2long.toLongID(person);
            // store the mapping for the user
            thing2long.storeMapping(userLong, person);
            // create a long from the like name
            long itemLong = thing2long.toLongID(likeName);
            // store the mapping for the item
            thing2long.storeMapping(itemLong, likeName);
            List<Preference> userPrefList;
            // otherwise create a new one.
            if ((userPrefList = preferecesOfUsers.get(userLong)) == null) {
                userPrefList = new ArrayList<Preference>();
                preferecesOfUsers.put(userLong, userPrefList);
            }
            // add the like that we just found to this user
            userPrefList.add(new GenericPreference(userLong, itemLong, 1));
            log.fine("Adding " + person + "(" + userLong + ") to " + likeName + "(" + itemLong + ")");
        }
        // create the corresponding mahout data structure from the map
        FastByIDMap<PreferenceArray> preferecesOfUsersFastMap = new FastByIDMap<PreferenceArray>();
        for (Entry<Long, List<Preference>> entry : preferecesOfUsers.entrySet()) {
            preferecesOfUsersFastMap.put(entry.getKey(), new GenericUserPreferenceArray(entry.getValue()));
        }
        // create a data model 
        dataModel = new GenericDataModel(preferecesOfUsersFastMap);
        // Instantiate the recommender
        recommender = new GenericBooleanPrefItemBasedRecommender(dataModel, new LogLikelihoodSimilarity(dataModel));
    } catch (URISyntaxException e) {
        log.log(Level.SEVERE, "Problem with the file URL", e);
    } catch (FileNotFoundException e) {
        log.log(Level.SEVERE, DATA_FILE_NAME + " was not found", e);
    } catch (IOException e) {
        log.log(Level.SEVERE, "Error during reading line of file", e);
    }
}