MyMediaLite
3.07
|
Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD) More...
Public Member Functions | |
override void | AddRatings (IRatings ratings) |
Add new ratings and perform incremental training. | |
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 float | ComputeObjective () |
Compute the regularized loss. | |
virtual void | Iterate () |
Run one iteration (= pass over the training data) | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
MatrixFactorization () | |
Default constructor. | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item. | |
IList< Tuple< int, float > > | Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null) |
Recommend items for a given user. | |
virtual System.Collections.Generic.IList < Tuple< int, float > > | Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null) |
override void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
override void | RemoveRatings (IDataSet ratings) |
Remove existing ratings and perform "incremental" training. | |
override void | RemoveUser (int user_id) |
Remove all feedback by one user. | |
virtual void | RetrainItem (int item_id) |
Updates the latent factors of an item. | |
virtual void | RetrainUser (int user_id) |
Updates the latent factors on a user. | |
override void | SaveModel (string filename) |
Save the model parameters to a file. | |
IList< Tuple< int, float > > | ScoreItems (IList< Tuple< int, float >> rated_items, IList< int > candidate_items) |
Rate a list of items given a list of ratings that represent a new user. | |
override string | ToString () |
Return a string representation of the recommender. | |
override void | Train () |
Learn the model parameters of the recommender from the training data. | |
override void | UpdateRatings (IRatings ratings) |
Update existing ratings and perform incremental training. | |
Protected Member Functions | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
virtual float[] | FoldIn (IList< Tuple< int, float >> rated_items) |
Compute parameters (latent factors) for a user represented by ratings. | |
virtual internal void | InitModel () |
Initialize the model data structure. | |
virtual void | Iterate (IList< int > rating_indices, bool update_user, bool update_item) |
Iterate once over rating data and adjust corresponding factors (stochastic gradient descent) | |
float | Predict (int user_id, int item_id, bool bound) |
virtual float | Predict (float[] user_vector, int item_id) |
Predict rating for a fold-in user and an item. | |
virtual void | UpdateLearnRate () |
Updates current_learnrate after each epoch. | |
Protected Attributes | |
internal float | current_learnrate |
The learn rate used for the current epoch. | |
float | global_bias |
The bias (global average) | |
internal Matrix< float > | item_factors |
Matrix containing the latent item factors. | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
internal Matrix< float > | user_factors |
Matrix containing the latent user factors. | |
Properties | |
float | Decay [get, set] |
Multiplicative learn rate decay. | |
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 (update step size) | |
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. | |
virtual float | Regularization [get, set] |
Regularization 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 |
Simple matrix factorization class, learning is performed by stochastic gradient descent (SGD)
Factorizing the observed rating values using a factor matrix for users and one for items.
NaN values in the model occur if values become too large or too small to be represented by the type float. If you encounter such problems, there are three ways to fix them: (1) (preferred) Use BiasedMatrixFactorization, which is more stable. (2) Change the range of rating values (1 to 5 works generally well with the default settings). (3) Decrease the learn_rate.
This recommender supports incremental updates.
MatrixFactorization | ( | ) | [inline] |
Default constructor.
override void AddRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Add new ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
virtual float ComputeObjective | ( | ) | [inline, virtual] |
Compute the regularized loss.
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SocialMF.
virtual float [] FoldIn | ( | IList< Tuple< int, float >> | rated_items | ) | [inline, protected, virtual] |
Compute parameters (latent factors) for a user represented by ratings.
rated_items | a list of (item ID, rating value) pairs |
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, GSVDPlusPlus, and SigmoidSVDPlusPlus.
virtual internal void InitModel | ( | ) | [inline, protected, virtual] |
Initialize the model data structure.
Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, BiasedMatrixFactorization, SVDPlusPlus, GSVDPlusPlus, and SocialMF.
virtual void Iterate | ( | ) | [inline, virtual] |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization.
virtual void Iterate | ( | IList< int > | rating_indices, |
bool | update_user, | ||
bool | update_item | ||
) | [inline, protected, virtual] |
Iterate once over rating data and adjust corresponding factors (stochastic gradient descent)
rating_indices | a list of indices pointing to the ratings to iterate over |
update_user | true if user factors to be updated |
update_item | true if item factors to be updated |
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidSVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, GSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SocialMF.
override void LoadModel | ( | string | filename | ) | [inline] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
virtual float Predict | ( | float[] | user_vector, |
int | item_id | ||
) | [inline, protected, virtual] |
Predict rating for a fold-in user and an item.
user_vector | a float vector representing the user |
item_id | the item ID |
Reimplemented in SVDPlusPlus, BiasedMatrixFactorization, and SigmoidCombinedAsymmetricFactorModel.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline] |
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 IRecommender.
Reimplemented in BiasedMatrixFactorization, GSVDPlusPlus, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
||
ICollection< int > | ignore_items = null , |
||
ICollection< int > | candidate_items = null |
||
) | [inherited] |
Recommend items for a given user.
user_id | the user ID |
n | the number of items to recommend, -1 for as many as possible |
ignore_items | collection if items that should not be returned; if null, use empty collection |
candidate_items | the candidate items to choose from; if null, use all items |
Implemented in WeightedEnsemble, and Ensemble.
override void RemoveItem | ( | int | item_id | ) | [inline, virtual] |
Remove all feedback by one item.
item_id | the item ID |
Reimplemented from IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
override void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual] |
Remove existing ratings and perform "incremental" training.
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
override void RemoveUser | ( | int | user_id | ) | [inline, virtual] |
Remove all feedback by one user.
user_id | the user ID |
Reimplemented from IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
virtual void RetrainItem | ( | int | item_id | ) | [inline, virtual] |
Updates the latent factors of an item.
item_id | the item ID |
Reimplemented in BiasedMatrixFactorization.
virtual void RetrainUser | ( | int | user_id | ) | [inline, virtual] |
Updates the latent factors on a user.
user_id | the user ID |
Reimplemented in BiasedMatrixFactorization, and SVDPlusPlus.
override void SaveModel | ( | string | filename | ) | [inline] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
IList<Tuple<int, float> > ScoreItems | ( | IList< Tuple< int, float >> | rated_items, |
IList< int > | candidate_items | ||
) | [inline] |
Rate a list of items given a list of ratings that represent a new user.
rated_items | the ratings (item IDs and rating values) representing the new user |
candidate_items | the items to be rated |
Implements IFoldInRatingPredictor.
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, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SigmoidSVDPlusPlus, and SocialMF.
virtual void UpdateLearnRate | ( | ) | [inline, protected, virtual] |
Updates current_learnrate after each epoch.
Reimplemented in BiasedMatrixFactorization.
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
internal float current_learnrate [protected] |
The learn rate used for the current epoch.
float global_bias [protected] |
The bias (global average)
internal Matrix<float> item_factors [protected] |
Matrix containing the latent item factors.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
internal Matrix<float> user_factors [protected] |
Matrix containing the latent user factors.
float Decay [get, set] |
Multiplicative learn rate decay.
Applied after each epoch (= pass over the whole dataset)
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 (update step size)
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.
virtual float Regularization [get, set] |
Regularization parameter.
Reimplemented in BiasedMatrixFactorization.
bool UpdateItems [get, set, inherited] |
true if items shall be updated when doing incremental updates
Set to false if you do not want any updates to the item model parameters when doing incremental updates.
Implements IIncrementalRecommender.
bool UpdateUsers [get, set, inherited] |
true if users shall be updated when doing incremental updates
Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.
Implements IIncrementalRecommender.