Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction). More...
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. |
Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction).
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.
user_id | the user ID | |
item_id | the item ID |
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.
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.
user_id | the user ID | |
item_id | the item ID |
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.
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.
float max_rating [protected] |
Maximum rating value.
float min_rating [protected] |
Minimum rating value.
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.
The rating data.
Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.