Matrix factorization with factor-wise learning. More...
Public Member Functions | |
virtual bool | CanPredict (int user_id, int item_id) |
Check whether a useful prediction can be made for a given user-item combination. | |
Object | Clone () |
create a shallow copy of the object | |
double | ComputeFit () |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data. | |
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 double | 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 | |
double | max_rating |
Maximum rating value. | |
double | 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 double | MaxRating [get, set] |
Maximum rating value. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
virtual double | 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). | |
virtual IRatings | Ratings [get, set] |
The rating data. | |
virtual double | Sensibility [get, set] |
Sensibility parameter (stopping criterion for parameter fitting). | |
virtual double | Shrinkage [get, set] |
Shrinkage parameter. | |
bool | UpdateItems [get, set] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates |
Matrix factorization with factor-wise learning.
Robert Bell, Yehuda Koren, Chris Volinsky: Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems, ACM Int. Conference on Knowledge Discovery and Data Mining (KDD'07), 2007.
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 can be made for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
double ComputeFit | ( | ) | [inline] |
Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
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 double 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.
double max_rating [protected, inherited] |
Maximum rating value.
double 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 double MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual double 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 in ItemKNN, TimeAwareRatingPredictor, and UserKNN.
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
bool UpdateItems [get, set, inherited] |
true if items shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default is true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.