RatingPredictor Class Reference

Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction). More...

Inheritance diagram for RatingPredictor:
IRatingPredictor IRecommender BiPolarSlopeOne CoClustering FactorWiseMatrixFactorization IncrementalRatingPredictor LatentFeatureLogLinearModel SlopeOne TimeAwareRatingPredictor Constant EntityAverage GlobalAverage KNN MatrixFactorization Random UserItemBaseline TimeAwareBaseline

List of all members.

Public Member Functions

virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination.
Object Clone ()
 create a shallow copy of the object
virtual void LoadModel (string file)
 Get the model parameters from a file.
abstract float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
virtual void SaveModel (string file)
 Save the model parameters to a file.
override string ToString ()
 Return a string representation of the recommender.
abstract void Train ()
 Learn the model parameters of the recommender from the training data.

Protected Attributes

float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

Properties

int MaxItemID [get, set]
 Maximum item ID.
virtual float MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual float MinRating [get, set]
 Minimum rating value.
virtual IRatings Ratings [get, set]
 The rating data.

Detailed Description

Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction).


Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
) [inline, virtual]

Check whether a useful prediction (i.e. not using a fallback/default answer) can be made for a given user-item combination.

It is up to the recommender implementor to decide when a prediction is useful, and to document it accordingly.

Parameters:
user_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in BiPolarSlopeOne, Constant, GlobalAverage, ItemAverage, Random, SlopeOne, and UserAverage.

Object Clone (  )  [inline]

create a shallow copy of the object

virtual void LoadModel ( string  filename  )  [inline, virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, ItemKNN, KNN, MatrixFactorization, Random, SigmoidSVDPlusPlus, SlopeOne, SVDPlusPlus, and UserItemBaseline.

abstract float Predict ( int  user_id,
int  item_id 
) [pure virtual]

Predict rating or score for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted score/rating for the given user-item combination

Implements IRecommender.

Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, LatentFeatureLogLinearModel, MatrixFactorization, Random, SigmoidSVDPlusPlus, SlopeOne, SVDPlusPlus, TimeAwareBaseline, UserAverage, UserItemBaseline, and UserKNN.

virtual void SaveModel ( string  filename  )  [inline, virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, Random, SlopeOne, SVDPlusPlus, and UserItemBaseline.

override string ToString (  )  [inline]

Return a string representation of the recommender.

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, CoClustering, Constant, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, LatentFeatureLogLinearModel, MatrixFactorization, SigmoidSVDPlusPlus, SocialMF, SVDPlusPlus, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.


Member Data Documentation

float max_rating [protected]

Maximum rating value.

float min_rating [protected]

Minimum rating value.

IRatings ratings [protected]

rating data


Property Documentation

int MaxItemID [get, set]

Maximum item ID.

virtual float MaxRating [get, set]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set]

Maximum user ID.

virtual float MinRating [get, set]

Minimum rating value.

Implements IRatingPredictor.

virtual IRatings Ratings [get, set]

The rating data.

Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.


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