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 RatingPredictor IIncrementalRatingPredictor IRecommender IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRating (int user_id, int item_id, double rating)
 Add a new rating and perform incremental training.
 BiasedMatrixFactorization ()
 Default constructor.
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 (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
override double ComputeLoss ()
 Compute the regularized loss.
override 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.
override void RemoveItem (int item_id)
 Remove an item from the recommender model, and delete all ratings of this item.
override void RemoveRating (int user_id, int item_id)
 Remove an existing rating and perform "incremental" training.
override void RemoveUser (int user_id)
 Remove a user from the recommender model, and delete all their ratings.
override void RetrainItem (int item_id)
 Updates the latent factors of an item.
override void RetrainUser (int user_id)
 Updates the latent factors on a user.
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.
override void UpdateRating (int user_id, int item_id, double rating)
 Update an existing rating and perform incremental training.

Protected Member Functions

override void AddItem (int item_id)
override void AddUser (int user_id)
override void InitModel ()
 Initialize the model data structure.
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).
double Predict (int user_id, int item_id, bool bound)

Protected Attributes

double global_bias
 The bias (global average).
double[] item_bias
 the item biases
Matrix< double > item_factors
 Matrix containing the latent item factors.
double last_loss = double.NegativeInfinity
 Loss for the last iteration, used by bold driver heuristics.
double max_rating
 Maximum rating value.
double min_rating
 Minimum rating value.
IRatings ratings
 rating data
double[] user_bias
 the user biases
Matrix< double > user_factors
 Matrix containing the latent user factors.

Properties

double BiasReg [get, set]
 regularization constant for the bias terms
bool BoldDriver [get, set]
 Use bold driver heuristics for learning rate adaption.
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.
double LearnRate [get, set]
 Learn rate.
int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double 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.
bool OptimizeMAE [get, set]
 If set to true, optimize model for MAE instead of RMSE.
virtual IRatings Ratings [get, set]
 The rating data.
double RegI [get, set]
 regularization constant for the item factors
double RegU [get, set]
 regularization constant for the user factors
override double Regularization [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

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

Per default optimizes for RMSE. Set OptimizeMAE to true if you want to optimize for MAE.

Literature:

This recommender supports incremental updates.


Constructor & Destructor Documentation

BiasedMatrixFactorization (  )  [inline]

Default constructor.


Member Function Documentation

override void AddRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Add a new rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Reimplemented from IncrementalRatingPredictor.

virtual bool CanPredict ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Check whether a useful prediction can be made for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in BiPolarSlopeOne, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

double ComputeFit (  )  [inline, inherited]

Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.

Returns:
the fit on the training data according to the optimization criterion; -1 if not implemented

Implements IIterativeModel.

override double ComputeLoss (  )  [inline, virtual]

Compute the regularized loss.

Returns:
the regularized loss

Reimplemented from MatrixFactorization.

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

Initialize the model data structure.

Reimplemented from MatrixFactorization.

override 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_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 from MatrixFactorization.

override void Iterate (  )  [inline, virtual]

Run one iteration (= pass over the training data).

Reimplemented from MatrixFactorization.

override void LoadModel ( string  filename  )  [inline]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from MatrixFactorization.

override double 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_id the user ID
item_id the item ID
Returns:
the predicted rating

Reimplemented from MatrixFactorization.

override void RemoveItem ( int  item_id  )  [inline, virtual]

Remove an item from the recommender model, and delete all ratings of this item.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
item_id the ID of the user to be removed

Reimplemented from MatrixFactorization.

override void RemoveRating ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Remove an existing rating and perform "incremental" training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item

Reimplemented from IncrementalRatingPredictor.

override void RemoveUser ( int  user_id  )  [inline, virtual]

Remove a user from the recommender model, and delete all their ratings.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
user_id the ID of the user to be removed

Reimplemented from MatrixFactorization.

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

Updates the latent factors of an item.

Parameters:
item_id the item ID

Reimplemented from MatrixFactorization.

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

Updates the latent factors on a user.

Parameters:
user_id the user ID

Reimplemented from MatrixFactorization.

override void SaveModel ( string  filename  )  [inline]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from MatrixFactorization.

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.

Reimplemented from MatrixFactorization.

override void UpdateRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Update an existing rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

double global_bias [protected, inherited]

The bias (global average).

double [] item_bias [protected]

the item biases

Matrix<double> item_factors [protected, inherited]

Matrix containing the latent item factors.

double last_loss = double.NegativeInfinity [protected]

Loss for the last iteration, used by bold driver heuristics.

double max_rating [protected, inherited]

Maximum rating value.

double min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data

double [] user_bias [protected]

the user biases

Matrix<double> user_factors [protected, inherited]

Matrix containing the latent user factors.


Property Documentation

double BiasReg [get, set]

regularization constant for the bias terms

bool BoldDriver [get, set]

Use bold driver heuristics for learning rate adaption.

Literature:

double InitMean [get, set, inherited]

Mean of the normal distribution used to initialize the factors.

double InitStdDev [get, set, inherited]

Standard deviation of the normal distribution used to initialize the factors.

double LearnRate [get, set, inherited]

Learn rate.

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual double MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

uint NumFactors [get, set, inherited]

Number of latent factors.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

bool OptimizeMAE [get, set]

If set to true, optimize model for MAE instead of RMSE.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in ItemKNN, and UserKNN.

double RegI [get, set]

regularization constant for the item factors

double RegU [get, set]

regularization constant for the user factors

override double Regularization [set]

Regularization parameter.

Reimplemented from MatrixFactorization.

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.


The documentation for this class was generated from the following file:
Generated on Sat Oct 8 18:11:35 2011 for MyMediaLite by  doxygen 1.6.3