MyMediaLite  3.03
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
MatrixFactorization Class Reference

Simple matrix factorization class, learning is performed by stochastic gradient descent. More...

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

List of all members.

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 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.

Protected Attributes

float global_bias
 The bias (global average)
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
Matrix< float > user_factors
 Matrix containing the latent user factors.

Properties

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.
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

Detailed Description

Simple matrix factorization class, learning is performed by stochastic gradient descent.

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) Change the learn_rate (decrease it if your range is larger than 1 to 5).

This recommender supports incremental updates.


Constructor & Destructor Documentation

MatrixFactorization ( ) [inline]

Default constructor.


Member Function Documentation

override void AddRatings ( IRatings  ratings) [inline, virtual]

Add new ratings and perform incremental training.

Parameters:
ratingsthe 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.

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

Implements IRecommender.

Reimplemented in 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.

Returns:
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.

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

Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SigmoidSVDPlusPlus.

virtual void InitModel ( ) [inline, protected, virtual]
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)

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 in BiasedMatrixFactorization, SVDPlusPlus, SigmoidSVDPlusPlus, 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.

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.

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

Reimplemented in BiasedMatrixFactorization.

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.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted rating

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, 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.

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) [inline, virtual]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Reimplemented from IncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization.

override void RemoveRatings ( IDataSet  ratings) [inline, virtual]

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) [inline, virtual]

Remove all feedback by one user.

Parameters:
user_idthe user ID

Reimplemented from IncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization.

virtual void RetrainItem ( int  item_id) [inline, virtual]

Updates the latent factors of an item.

Parameters:
item_idthe item ID

Reimplemented in BiasedMatrixFactorization.

virtual void RetrainUser ( int  user_id) [inline, virtual]

Updates the latent factors on a user.

Parameters:
user_idthe user ID

Reimplemented in BiasedMatrixFactorization, and SVDPlusPlus.

override void SaveModel ( string  filename) [inline]

Save the model parameters to a file.

Parameters:
filenamethe 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.

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.

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.

override void UpdateRatings ( IRatings  ratings) [inline, virtual]

Update existing ratings and perform incremental training.

Parameters:
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

float global_bias [protected]

The bias (global average)

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.

IRatings ratings [protected, inherited]

rating data

Matrix<float> user_factors [protected]

Matrix containing the latent user factors.


Property Documentation

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.

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.

virtual IRatings Ratings [get, set, inherited]

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.


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