Java Rating类代码示例

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
private SparseVector makeUserVector(long user) {
    // Get the user's ratings
    List<Rating> userRatings = dao.getEventsForUser(user, Rating.class);
    if (userRatings == null) {
        // the user doesn't exist
        return SparseVector.empty();
    }

    // Create a new vector over tags to accumulate the user profile
    MutableSparseVector profile = model.newTagVector();
    // Fill it with 0's initially - they don't like anything
    profile.fill(0);

    // Iterate over the user's ratings to build their profile
    for (Rating r: userRatings) {
        // In LensKit, ratings are expressions of preference
        Preference p = r.getPreference();
        // We'll never have a null preference. But in LensKit, ratings can have null
        // preferences to express the user unrating an item
        if (p != null && p.getValue() >= 3.5) {
            // The user likes this item!
            // Get the item's vector and add it to the user's profile
            SparseVector iv = model.getItemVector(p.getItemId());
            profile.add(iv);
        }
    }

    // The profile is accumulated, return it.
    // It is good practice to return a frozen vector.
    return profile.freeze();
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Get a user's ratings.
 * @param user The user ID.
 * @return The ratings to retrieve.
 */
private SparseVector getUserRatingVector(long user) {
    UserHistory<Rating> history = userEvents.getEventsForUser(user, Rating.class);
    if (history == null) {
        history = History.forUser(user);
    }

    return RatingVectorUserHistorySummarizer.makeRatingVector(history);
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Get a user's rating vector.
 * @param user The user ID.
 * @return The rating vector.
 */
private SparseVector getUserRatingVector(long user) {
    UserHistory<Rating> history = userDao.getEventsForUser(user, Rating.class);
    if (history == null) {
        history = History.forUser(user);
    }
    return RatingVectorUserHistorySummarizer.makeRatingVector(history);
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
private SparseVector makeUserVector(long user) {
    // Get the user's ratings
    List<Rating> userRatings = dao.getEventsForUser(user, Rating.class);
    if (userRatings == null) {
        // the user doesn't exist
        return SparseVector.empty();
    }

    // Create a new vector over tags to accumulate the user profile
    MutableSparseVector profile = model.newTagVector();
    // Fill it with 0's initially - they don't like anything
    profile.fill(0);

    // Iterate over the user's ratings to build their profile
    for (Rating r: userRatings) {
        // In LensKit, ratings are expressions of preference
        Preference p = r.getPreference();
        // We'll never have a null preference. But in LensKit, ratings can have null
        // preferences to express the user unrating an item
        if (p != null && p.getValue() >= 3.5) {
            
        	SparseVector sparseVectorForItem = model.getItemVector(p.getItemId());
        	profile.add(sparseVectorForItem);
        }
    }

    // The profile is accumulated, return it.
    // It is good practice to return a frozen vector.
    return profile.freeze();
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Get a user's ratings.
 * @param user The user ID.
 * @return The ratings to retrieve.
 */
private SparseVector getUserRatingVector(long user) {
	UserHistory<Rating> history = userEvents.getEventsForUser(user, Rating.class);
	if (history == null) {
		history = History.forUser(user);
	}

	return RatingVectorUserHistorySummarizer.makeRatingVector(history);
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Get a user's rating vector
 * @param user The user ID
 * @return The rating vector
 */
private SparseVector getUserRatingVector(long user) {
    UserHistory<Rating> history = userDao.getEventsForUser(user, Rating.class);
    if (history == null) {
        history = History.forUser(user);
    }
    return RatingVectorUserHistorySummarizer.makeRatingVector(history);
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Build a rating matrix from the rating data.  Each user's ratings are first normalized
 * by subtracting a baseline score (usually a mean).
 *
 * @param userMapping The index mapping of user IDs to column numbers.
 * @param itemMapping The index mapping of item IDs to row numbers.
 * @return A matrix storing the <i>normalized</i> user ratings.
 */
private RealMatrix createRatingMatrix(IdIndexMapping userMapping, IdIndexMapping itemMapping) {
    final int nusers = userMapping.size();
    final int nitems = itemMapping.size();

    // Create a matrix with users on rows and items on columns
    logger.info("creating {} by {} rating matrix", nusers, nitems);
    RealMatrix matrix = MatrixUtils.createRealMatrix(nusers, nitems);

    // populate it with data
    Cursor<UserHistory<Event>> users = userEventDAO.streamEventsByUser();
    try {
        for (UserHistory<Event> user: users) {
            // Get the row number for this user
            int u = userMapping.getIndex(user.getUserId());
            MutableSparseVector ratings = Ratings.userRatingVector(user.filter(Rating.class));
            MutableSparseVector baselines = MutableSparseVector.create(ratings.keySet());
            baselineScorer.score(user.getUserId(), baselines);
            
            for(VectorEntry v : ratings.fast())
            {
            	matrix.setEntry(u, itemMapping.getIndex(v.getKey()), v.getValue() - baselineScorer.score(user.getUserId(), v.getKey()));
            }
            
        }
    } finally {
        users.close();
    }

    return matrix;
}
 

import org.grouplens.lenskit.data.event.Rating; //导入依赖的package包/类
/**
 * Get a user's ratings.
 * 
 * @param user
 *            The user ID.
 * @return The ratings to retrieve.
 */
private SparseVector getUserRatingVector(long user) {
	UserHistory<Rating> history = userEvents.getEventsForUser(user,
			Rating.class);
	if (history == null) {
		history = History.forUser(user);
	}

	return RatingVectorUserHistorySummarizer.makeRatingVector(history);
}