/**
* 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);
// TODO Populate this user's row with their ratings, minus the baseline scores
for (VectorEntry entry : ratings.fast(State.SET)) {
long itemid = entry.getKey();
int i = itemMapping.getIndex(itemid);
double rating = entry.getValue();
double baseline = baselines.get(itemid);
matrix.setEntry(u, i, rating - baseline);
}
}
} finally {
users.close();
}
return matrix;
}
/**
* 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);
}
/**
* It is used to generate rating list from UserEventDAO.
*
* @param uid The user ID.
* @param dao The UserEventDAO.
* @return The VectorEntry list of rating.
*/
private SparseVector makeUserVector(long uid, UserEventDAO dao) {
UserHistory<Rating> history = dao.getEventsForUser(uid, Rating.class);
SparseVector vector = null;
if (history != null) {
vector = RatingVectorUserHistorySummarizer.makeRatingVector(history);
}
return vector;
}
@Override
protected TestUser getUserResults(long uid) {
Preconditions.checkState(recommender != null, "recommender not built");
UserHistory<Event> userData = userEvents.getEventsForUser(uid);
return new LenskitTestUser(recommender, userData);
}
