FactorWiseMatrixFactorization Class Reference

Matrix factorization with factor-wise learning. More...

Inheritance diagram for FactorWiseMatrixFactorization:
RatingPredictor IIterativeModel IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

virtual void AddRating (int user_id, int item_id, double rating)
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 (RMSE) on the training data.
 FactorWiseMatrixFactorization ()
 Default constructor.
virtual 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.
virtual void RemoveItem (int item_id)
virtual void RemoveRating (int user_id, int item_id)
virtual void RemoveUser (int user_id)
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.
virtual void UpdateRating (int user_id, int item_id, double rating)

Protected Member Functions

virtual void AddItem (int item_id)
virtual void AddUser (int user_id)
override void InitModel ()
 Initialize the model data structure.

Protected Attributes

double max_rating
 The max rating value.
double min_rating
 The min rating value.
IRatings ratings
 rating data

Properties

double InitMean [get, set]
 Mean of the normal distribution used to initialize the factors.
double InitStdev [get, set]
 Standard deviation of the normal distribution used to initialize the factors.
int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 The max rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 The min rating value.
uint NumFactors [get, set]
 Number of latent factors.
uint NumIter [get, set]
 Number of iterations (in this case: number of latent factors).
virtual IRatings Ratings [get, set]
 The rating data.
virtual double Sensibility [get, set]
 Sensibility parameter (stopping criterion for parameter fitting).
virtual double Shrinkage [get, set]
 Shrinkage 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 factor-wise learning.

Robert Bell, Yehuda Koren, Chris Volinsky: Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems, ACM Int. Conference on Knowledge Discovery and Data Mining (KDD'07), 2007.

This recommender does NOT support incremental updates.


Constructor & Destructor Documentation

Default constructor.


Member Function Documentation

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

create a shallow copy of the object

double ComputeFit (  ) 

Compute the fit (RMSE) on the training data.

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

Implements IIterativeModel.

override void InitModel (  )  [protected, virtual]

Initialize the model data structure.

Reimplemented from RatingPredictor.

virtual void Iterate (  )  [virtual]

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

Implements IIterativeModel.

override void LoadModel ( string  filename  )  [virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements RatingPredictor.

override double Predict ( int  user_id,
int  item_id 
) [virtual]

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

Implements RatingPredictor.

override void SaveModel ( string  filename  )  [virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements RatingPredictor.

override string ToString (  ) 

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.


Member Data Documentation

double max_rating [protected, inherited]

The max rating value.

double min_rating [protected, inherited]

The min rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

double InitMean [get, set]

Mean of the normal distribution used to initialize the factors.

double InitStdev [get, set]

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

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual double MaxRating [get, set, inherited]

The max rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

The min rating value.

Implements IRatingPredictor.

uint NumFactors [get, set]

Number of latent factors.

uint NumIter [get, set]

Number of iterations (in this case: number of latent factors).

Implements IIterativeModel.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in ItemKNN, and UserKNN.

virtual double Sensibility [get, set]

Sensibility parameter (stopping criterion for parameter fitting).

epsilon in the Bell et al. paper

virtual double Shrinkage [get, set]

Shrinkage parameter.

alpha in the Bell et al. paper

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:
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