MyMediaLite
3.01
|
Matrix factorization with factor-wise learning. 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. | |
FactorWiseMatrixFactorization () | |
Default constructor. | |
virtual void | Iterate () |
Run one iteration (= pass over the training data) | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item. | |
override void | SaveModel (string filename) |
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 | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the factors. | |
double | InitStdev [get, set] |
Standard deviation of the normal distribution used to initialize the factors. | |
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 (in this case: number of latent factors) | |
override IRatings | Ratings [set] |
The rating data. | |
float | RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor | |
float | RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor | |
virtual double | Sensibility [get, set] |
Sensibility parameter (stopping criterion for parameter fitting) | |
virtual double | Shrinkage [get, set] |
Shrinkage parameter. |
Matrix factorization with factor-wise learning.
Similar to the approach described in Simon Funk's seminal blog post: http://sifter.org/~simon/journal/20061211.html
Literature:
This recommender does NOT support incremental updates.
FactorWiseMatrixFactorization | ( | ) | [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.
virtual void Iterate | ( | ) | [inline, virtual] |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
override void LoadModel | ( | string | filename | ) | [inline, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from RatingPredictor.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
Predict the rating of a given user for a given item.
If the user or the item are not known to the recommender, the global effects prediction is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.
user_id | the user ID |
item_id | the item ID |
Implements RatingPredictor.
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from RatingPredictor.
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.
double InitMean [get, set] |
Mean of the normal distribution used to initialize the factors.
double InitStdev [get, set] |
Standard deviation of the normal distribution used to initialize the factors.
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 (in this case: number of latent factors)
Implements IIterativeModel.
The rating data.
Reimplemented from RatingPredictor.
float RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor
float RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor
virtual double Sensibility [get, set] |
Sensibility parameter (stopping criterion for parameter fitting)
epsilon in the Bell et al. paper
virtual double Shrinkage [get, set] |
Shrinkage parameter.
alpha in the Bell et al. paper