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java.lang.Objectprea.recommender.baseline.BaselineRecommender
public abstract class BaselineRecommender
This is an abstract class implementing five baselines, including constant model, overall average, user average, item average, and random.
Field Summary | |
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int |
itemCount
The number of items. |
double |
maxValue
Maximum value of rating, existing in the dataset. |
double |
minValue
Minimum value of rating, existing in the dataset. |
protected SparseMatrix |
rateMatrix
Rating matrix for each user (row) and item (column) |
int |
userCount
The number of users. |
Constructor Summary | |
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BaselineRecommender(int uc,
int ic,
double max,
double min)
Construct a constant model with the given data. |
Method Summary | |
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void |
buildModel(SparseMatrix rm)
Build a model with given training set. |
EvaluationMetrics |
evaluate(SparseMatrix testMatrix)
Evaluate the designated algorithm with the given test data. |
(package private) abstract double |
predict(int userId,
int itemId)
Predict a rating for the given user and item. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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protected SparseMatrix rateMatrix
public int userCount
public int itemCount
public double maxValue
public double minValue
Constructor Detail |
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public BaselineRecommender(int uc, int ic, double max, double min)
uc
- The number of users in the dataset.ic
- The number of items in the dataset.max
- The maximum rating value in the dataset.min
- The minimum rating value in the dataset.Method Detail |
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public void buildModel(SparseMatrix rm)
buildModel
in interface Recommender
rm
- Training data set.abstract double predict(int userId, int itemId)
userId
- The target user.itemId
- The target item.
public EvaluationMetrics evaluate(SparseMatrix testMatrix)
evaluate
in interface Recommender
testMatrix
- The matrix with test data points used for evaluation.
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