MyMediaLite
3.11
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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 More... | |
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 More... | |
Object | Clone () |
create a shallow copy of the object More... | |
virtual float | ComputeObjective () |
Compute the regularized loss More... | |
virtual void | Iterate () |
Run one iteration (= pass over the training data) More... | |
override void | LoadModel (string filename) |
Get the model parameters from a file More... | |
MatrixFactorization () | |
Default constructor More... | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item More... | |
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 More... | |
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 More... | |
override void | RemoveRatings (IDataSet ratings) |
Remove existing ratings and perform "incremental" training More... | |
override void | RemoveUser (int user_id) |
Remove all feedback by one user More... | |
virtual void | RetrainItem (int item_id) |
Updates the latent factors of an item More... | |
virtual void | RetrainUser (int user_id) |
Updates the latent factors on a user More... | |
override void | SaveModel (string filename) |
Save the model parameters to a file More... | |
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 More... | |
override string | ToString () |
Return a string representation of the recommender More... | |
override void | Train () |
Learn the model parameters of the recommender from the training data More... | |
override void | UpdateRatings (IRatings ratings) |
Update existing ratings and perform incremental training More... | |
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 More... | |
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) More... | |
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 More... | |
virtual void | UpdateLearnRate () |
Updates current_learnrate after each epoch More... | |
Protected Attributes | |
float | global_bias |
The bias (global average) More... | |
float | max_rating |
Maximum rating value More... | |
float | min_rating |
Minimum rating value More... | |
IRatings | ratings |
rating data More... | |
Properties | |
float | Decay [get, set] |
Multiplicative learn rate decay More... | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the factors More... | |
double | InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the factors More... | |
float | LearnRate [get, set] |
Learn rate (update step size) More... | |
int | MaxItemID [get, set] |
Maximum item ID More... | |
virtual float | MaxRating [get, set] |
Maximum rating value More... | |
int | MaxUserID [get, set] |
Maximum user ID More... | |
virtual float | MinRating [get, set] |
Minimum rating value More... | |
uint | NumFactors [get, set] |
Number of latent factors More... | |
uint | NumIter [get, set] |
Number of iterations over the training data More... | |
virtual IRatings | Ratings [get, set] |
The rating data More... | |
virtual float | Regularization [get, set] |
Regularization parameter More... | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
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.
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inline |
Default constructor
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inlinevirtual |
Add new ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtualinherited |
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.
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inlineinherited |
create a shallow copy of the object
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inlinevirtual |
Compute the regularized loss
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SocialMF.
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inlineprotectedvirtual |
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.
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inlinevirtual |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization.
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inlineprotectedvirtual |
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.
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inline |
Get the model parameters from a file
filename | the name of the file to read from |
Implements IRecommender.
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inlineprotectedvirtual |
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.
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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.
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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.
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inlinevirtual |
Remove all feedback by one item
item_id | the item ID |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtual |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtual |
Remove all feedback by one user
user_id | the user ID |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtual |
Updates the latent factors of an item
item_id | the item ID |
Reimplemented in BiasedMatrixFactorization.
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inlinevirtual |
Updates the latent factors on a user
user_id | the user ID |
Reimplemented in BiasedMatrixFactorization, and SVDPlusPlus.
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inline |
Save the model parameters to a file
filename | the name of the file to write to |
Implements IRecommender.
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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.
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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.
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inline |
Learn the model parameters of the recommender from the training data
Implements IRecommender.
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inlineprotectedvirtual |
Updates current_learnrate after each epoch
Reimplemented in BiasedMatrixFactorization.
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inlinevirtual |
Update existing ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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protected |
The bias (global average)
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protectedinherited |
Maximum rating value
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protectedinherited |
Minimum rating value
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protectedinherited |
rating data
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getset |
Multiplicative learn rate decay
Applied after each epoch (= pass over the whole dataset)
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getset |
Mean of the normal distribution used to initialize the factors
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getset |
Standard deviation of the normal distribution used to initialize the factors
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getset |
Learn rate (update step size)
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum rating value
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getsetinherited |
Maximum user ID
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getsetinherited |
Minimum rating value
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getset |
Number of latent factors
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getset |
Number of iterations over the training data
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getset |
Regularization parameter