MyMediaLite
3.02
|
Latent-feature log linear model. 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 | |
float | ComputeObjective () |
Compute the current optimization objective (usually loss plus regularization term) of the model. | |
void | Iterate () |
Run one iteration (= pass over the training data) | |
LatentFeatureLogLinearModel () | |
Default constructor. | |
virtual void | LoadModel (string file) |
Get the model parameters from a file. | |
override 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. | |
override 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 | |
float | BiasLearnRate [get, set] |
Learn rate factor for the bias terms. | |
float | BiasReg [get, set] |
regularization factor for the bias terms | |
bool | FrequencyRegularization [get, set] |
Regularization based on rating frequency. | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the factors. | |
double | InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the factors. | |
float | LearnRate [get, set] |
Learn rate. | |
OptimizationTarget | Loss [get, set] |
The optimization target. | |
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. | |
uint | NumFactors [get, set] |
Number of latent factors. | |
uint | NumIter [get, set] |
Number of iterations over the training data. | |
virtual IRatings | Ratings [get, set] |
The rating data. | |
float | RegI [get, set] |
regularization constant for the item factors | |
float | RegU [get, set] |
regularization constant for the user factors |
Latent-feature log linear model.
Literature:
This recommender supports incremental updates.
LatentFeatureLogLinearModel | ( | ) | [inline] |
Default constructor.
virtual bool CanPredict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual, inherited] |
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, SlopeOne, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
float ComputeObjective | ( | ) | [inline] |
Compute the current optimization objective (usually loss plus regularization term) of the model.
Implements IIterativeModel.
void Iterate | ( | ) | [inline] |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
virtual void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements IRecommender.
Reimplemented in MatrixFactorization, BiasedMatrixFactorization, CoClustering, SVDPlusPlus, FactorWiseMatrixFactorization, UserItemBaseline, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, ItemKNN, NaiveBayes, SlopeOne, EntityAverage, KNN, GlobalAverage, Constant, and Random.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID |
item_id | the item ID |
Implements RatingPredictor.
virtual void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements IRecommender.
Reimplemented in MatrixFactorization, BiasedMatrixFactorization, CoClustering, SVDPlusPlus, FactorWiseMatrixFactorization, UserItemBaseline, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, EntityAverage, KNN, GlobalAverage, Constant, and Random.
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.
Reimplemented from RatingPredictor.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
float BiasLearnRate [get, set] |
Learn rate factor for the bias terms.
float BiasReg [get, set] |
regularization factor for the bias terms
bool FrequencyRegularization [get, set] |
Regularization based on rating frequency.
Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. As described in the paper by Menon and Elkan.
double InitMean [get, set] |
Mean of the normal distribution used to initialize the factors.
double InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the factors.
float LearnRate [get, set] |
Learn rate.
OptimizationTarget Loss [get, set] |
The optimization target.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual float MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual float MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
uint NumFactors [get, set] |
Number of latent factors.
uint NumIter [get, set] |
Number of iterations over the training data.
Implements IIterativeModel.
The rating data.
Implements IRatingPredictor.
Reimplemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, ItemKNN, and UserKNN.
float RegI [get, set] |
regularization constant for the item factors
float RegU [get, set] |
regularization constant for the user factors