Matrix factorization with factor-wise learning. More...
Public Member Functions | |
virtual void | AddRating (int user_id, int item_id, double rating) |
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 (RMSE) 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. | |
virtual void | RemoveItem (int item_id) |
virtual void | RemoveRating (int user_id, int item_id) |
virtual void | RemoveUser (int user_id) |
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. | |
virtual void | UpdateRating (int user_id, int item_id, double rating) |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
override void | InitModel () |
Initialize the model data structure. | |
Protected Attributes | |
double | max_rating |
The max rating value. | |
double | min_rating |
The min 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] |
The max rating value. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
virtual double | MinRating [get, set] |
The min 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.
Default constructor.
virtual bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [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 | ( | ) | [inherited] |
create a shallow copy of the object
double ComputeFit | ( | ) |
Compute the fit (RMSE) on the training data.
Implements IIterativeModel.
override void InitModel | ( | ) | [protected, virtual] |
Initialize the model data structure.
Reimplemented from RatingPredictor.
virtual void Iterate | ( | ) | [virtual] |
Run one iteration (= pass over the training data).
Implements IIterativeModel.
override void LoadModel | ( | string | filename | ) | [virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements RatingPredictor.
override double Predict | ( | int | user_id, | |
int | item_id | |||
) | [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 average 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 | ) | [virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements RatingPredictor.
override string ToString | ( | ) |
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.
double max_rating [protected, inherited] |
The max rating value.
double min_rating [protected, inherited] |
The min 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] |
The max rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual double MinRating [get, set, inherited] |
The min 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.
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.