MyMediaLite  3.11
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BiasedMatrixFactorization Class Reference

Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent More...

Inheritance diagram for BiasedMatrixFactorization:
MatrixFactorization IncrementalRatingPredictor IIterativeModel IFoldInRatingPredictor RatingPredictor IIncrementalRatingPredictor IRatingPredictor Recommender IRatingPredictor IRatingPredictor IIncrementalRecommender IRecommender IRecommender IRecommender IRecommender SigmoidCombinedAsymmetricFactorModel SigmoidItemAsymmetricFactorModel SigmoidUserAsymmetricFactorModel SocialMF

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings and perform incremental training More...
 
 BiasedMatrixFactorization ()
 Default constructor 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...
 
override float ComputeObjective ()
 Compute the regularized loss More...
 
override void Iterate ()
 Run one iteration (= pass over the training data) More...
 
override void LoadModel (string filename)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination 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...
 
override void RetrainItem (int item_id)
 Updates the latent factors of an item More...
 
override 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)
 
double ComputeLoss ()
 Computes the value of the loss function that is currently being optimized More...
 
override float[] FoldIn (IList< Tuple< int, float >> rated_items)
 Compute parameters (latent factors) for a user represented by ratings More...
 
override 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)
 
override float Predict (float[] user_vector, int item_id)
 Predict rating for a fold-in user and an item More...
 
void SetupLoss ()
 Set up the common part of the error gradient of the loss function to optimize More...
 
override void UpdateLearnRate ()
 Updates current_learnrate after each epoch More...
 

Protected Attributes

Func< double, double, float > compute_gradient_common
 delegate to compute the common term of the error gradient More...
 
const int FOLD_IN_BIAS_INDEX = 0
 Index of the bias term in the user vector representation for fold-in More...
 
const int FOLD_IN_FACTORS_START = 1
 Start index of the user factors in the user vector representation for fold-in More...
 
float global_bias
 The bias (global average) More...
 
double last_loss = double.NegativeInfinity
 Loss for the last iteration, used by bold driver heuristics More...
 
float max_rating
 Maximum rating value More...
 
float min_rating
 Minimum rating value More...
 
float rating_range_size
 size of the interval of valid ratings More...
 
IRatings ratings
 rating data More...
 

Properties

float BiasLearnRate [get, set]
 Learn rate factor for the bias terms More...
 
float BiasReg [get, set]
 regularization factor for the bias terms More...
 
bool BoldDriver [get, set]
 Use bold driver heuristics for learning rate adaption More...
 
float Decay [get, set]
 Multiplicative learn rate decay More...
 
bool FrequencyRegularization [get, set]
 Regularization based on rating frequency 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...
 
OptimizationTarget Loss [get, set]
 The optimization target More...
 
int MaxItemID [get, set]
 Maximum item ID More...
 
virtual float MaxRating [get, set]
 Maximum rating value More...
 
int MaxThreads [get, set]
 the maximum number of threads to use More...
 
int MaxUserID [get, set]
 Maximum user ID More...
 
virtual float MinRating [get, set]
 Minimum rating value More...
 
bool NaiveParallelization [get, set]
 Use 'naive' parallelization strategy instead of conflict-free 'distributed' SGD 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...
 
float RegI [get, set]
 regularization constant for the item factors More...
 
float RegU [get, set]
 regularization constant for the user factors More...
 
override float Regularization [set]
 
bool UpdateItems [get, set]
 
bool UpdateUsers [get, set]
 

Detailed Description

Matrix factorization with explicit user and item bias, learning is performed by stochastic gradient descent

Per default optimizes for RMSE. Alternatively, you can set the Loss property to MAE or LogisticLoss. If set to log likelihood and with binary ratings, the recommender implements a simple version Menon and Elkan's LFL model, which predicts binary labels, has no advanced regularization, and uses no side information.

This recommender makes use of multi-core machines if requested. Just set MaxThreads to a large enough number (usually multiples of the number of available cores). The parallelization is based on ideas presented in the paper by Gemulla et al.

Literature:

This recommender supports incremental updates. See the paper by Rendle and Schmidt-Thieme.

Constructor & Destructor Documentation

Default constructor

Member Function Documentation

override void AddRatings ( IRatings  ratings)
inlinevirtualinherited

Add new ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

virtual bool CanPredict ( int  user_id,
int  item_id 
)
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.

Parameters
user_idthe user ID
item_idthe item ID
Returns
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( )
inlineinherited

create a shallow copy of the object

double ComputeLoss ( )
inlineprotected

Computes the value of the loss function that is currently being optimized

Returns
the loss
override float ComputeObjective ( )
inlinevirtual

Compute the regularized loss

Returns
the regularized loss

Reimplemented from MatrixFactorization.

Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SocialMF.

override float [] FoldIn ( IList< Tuple< int, float >>  rated_items)
inlineprotectedvirtual

Compute parameters (latent factors) for a user represented by ratings

Returns
a vector of latent factors
Parameters
rated_itemsa list of (item ID, rating value) pairs

Reimplemented from MatrixFactorization.

Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SigmoidItemAsymmetricFactorModel.

override void Iterate ( )
inlinevirtual

Run one iteration (= pass over the training data)

Reimplemented from MatrixFactorization.

override void Iterate ( IList< int >  rating_indices,
bool  update_user,
bool  update_item 
)
inlineprotectedvirtual

Iterate once over rating data and adjust corresponding factors (stochastic gradient descent)

Parameters
rating_indicesa list of indices pointing to the ratings to iterate over
update_usertrue if user factors to be updated
update_itemtrue if item factors to be updated

Reimplemented from MatrixFactorization.

Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SocialMF.

override void LoadModel ( string  filename)
inline

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Implements IRecommender.

override float Predict ( int  user_id,
int  item_id 
)
inline

Predict rating or score for a given user-item combination

Parameters
user_idthe user ID
item_idthe item ID
Returns
the predicted score/rating for the given user-item combination

Implements IRecommender.

override float Predict ( float[]  user_vector,
int  item_id 
)
inlineprotectedvirtual

Predict rating for a fold-in user and an item

Parameters
user_vectora float vector representing the user
item_idthe item ID
Returns
the predicted rating

Reimplemented from MatrixFactorization.

Reimplemented in SigmoidCombinedAsymmetricFactorModel.

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

Parameters
user_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe candidate items to choose from; if null, use all items
Returns
a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void RemoveItem ( int  item_id)
inlinevirtual

Remove all feedback by one item

Parameters
item_idthe item ID

Reimplemented from IncrementalRatingPredictor.

override void RemoveRatings ( IDataSet  ratings)
inlinevirtualinherited

Remove existing ratings and perform "incremental" training

Parameters
ratingsthe user and item IDs of the ratings to be removed

Reimplemented from IncrementalRatingPredictor.

override void RemoveUser ( int  user_id)
inlinevirtual

Remove all feedback by one user

Parameters
user_idthe user ID

Reimplemented from IncrementalRatingPredictor.

override void RetrainItem ( int  item_id)
inlinevirtual

Updates the latent factors of an item

Parameters
item_idthe item ID

Reimplemented from MatrixFactorization.

override void RetrainUser ( int  user_id)
inlinevirtual

Updates the latent factors on a user

Parameters
user_idthe user ID

Reimplemented from MatrixFactorization.

override void SaveModel ( string  filename)
inline

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Implements IRecommender.

IList<Tuple<int, float> > ScoreItems ( IList< Tuple< int, float >>  rated_items,
IList< int >  candidate_items 
)
inlineinherited

Rate a list of items given a list of ratings that represent a new user

Returns
a list of int and float pairs, representing item IDs and predicted ratings
Parameters
rated_itemsthe ratings (item IDs and rating values) representing the new user
candidate_itemsthe items to be rated

Implements IFoldInRatingPredictor.

void SetupLoss ( )
inlineprotected

Set up the common part of the error gradient of the loss function to optimize

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.

override void Train ( )
inline

Learn the model parameters of the recommender from the training data

Implements IRecommender.

override void UpdateLearnRate ( )
inlineprotectedvirtual

Updates current_learnrate after each epoch

Reimplemented from MatrixFactorization.

override void UpdateRatings ( IRatings  ratings)
inlinevirtualinherited

Update existing ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

Member Data Documentation

Func<double, double, float> compute_gradient_common
protected

delegate to compute the common term of the error gradient

const int FOLD_IN_BIAS_INDEX = 0
protected

Index of the bias term in the user vector representation for fold-in

const int FOLD_IN_FACTORS_START = 1
protected

Start index of the user factors in the user vector representation for fold-in

float global_bias
protectedinherited

The bias (global average)

double last_loss = double.NegativeInfinity
protected

Loss for the last iteration, used by bold driver heuristics

float max_rating
protectedinherited

Maximum rating value

float min_rating
protectedinherited

Minimum rating value

float rating_range_size
protected

size of the interval of valid ratings

IRatings ratings
protectedinherited

rating data

Property Documentation

float BiasLearnRate
getset

Learn rate factor for the bias terms

float BiasReg
getset

regularization factor for the bias terms

bool BoldDriver
getset

Use bold driver heuristics for learning rate adaption

Literature:

float Decay
getsetinherited

Multiplicative learn rate decay

Applied after each epoch (= pass over the whole dataset)

bool FrequencyRegularization
getset

Regularization based on rating frequency

Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. As described in the paper by Menon and Elkan.

double InitMean
getsetinherited

Mean of the normal distribution used to initialize the factors

double InitStdDev
getsetinherited

Standard deviation of the normal distribution used to initialize the factors

float LearnRate
getsetinherited

Learn rate (update step size)

OptimizationTarget Loss
getset

The optimization target

int MaxItemID
getsetinherited

Maximum item ID

virtual float MaxRating
getsetinherited

Maximum rating value

int MaxThreads
getset

the maximum number of threads to use

For parallel learning, set this number to a multiple of the number of available cores/CPUs

int MaxUserID
getsetinherited

Maximum user ID

virtual float MinRating
getsetinherited

Minimum rating value

bool NaiveParallelization
getset

Use 'naive' parallelization strategy instead of conflict-free 'distributed' SGD

The exact sequence of updates depends on the thread scheduling. If you want reproducible results, e.g. when setting –random-seed=N, do NOT set this property.

uint NumFactors
getsetinherited

Number of latent factors

uint NumIter
getsetinherited

Number of iterations over the training data

virtual IRatings Ratings
getsetinherited

The rating data

float RegI
getset

regularization constant for the item factors

float RegU
getset

regularization constant for the user factors


The documentation for this class was generated from the following file: