WRMF Class Reference

Weighted matrix factorization method proposed by Hu et al. and Pan et al. More...

Inheritance diagram for WRMF:
MF IncrementalItemRecommender IIterativeModel ItemRecommender IIncrementalItemRecommender IRecommender IRecommender

List of all members.

Public Member Functions

virtual void AddFeedback (int user_id, int item_id)
 Add a positive feedback event.
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
override double ComputeFit ()
 Computes the fit (optimization criterion) on the training data.
override void Iterate ()
 Iterate once over the data.
override void LoadModel (string file)
 Get the model parameters from a file.
override double Predict (int user_id, int item_id)
 Predict the weight for a given user-item combination.
virtual void RemoveFeedback (int user_id, int item_id)
 Remove all feedback events by the given user-item combination.
virtual void RemoveItem (int item_id)
 Remove all feedback by one item.
virtual void RemoveUser (int user_id)
 Remove all feedback by one user.
override void SaveModel (string file)
 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.

Protected Member Functions

virtual void AddItem (int item_id)
virtual void AddUser (int user_id)
virtual void InitModel ()
virtual void Optimize (IBooleanMatrix data, Matrix< double > W, Matrix< double > H)
 Optimizes the specified data.

Protected Attributes

Matrix< double > item_factors
 Latent item factor matrix.
int num_factors = 10
 Number of latent factors per user/item.
Matrix< double > user_factors
 Latent user factor matrix.

Properties

double CPos [get, set]
 C position: the weight/confidence that is put on positive observations.
virtual IPosOnlyFeedback Feedback [get, set]
 the feedback data to be used for training
double InitMean [get, set]
 Mean of the normal distribution used to initialize the latent factors.
double InitStdDev [get, set]
 Standard deviation of the normal distribution used to initialize the latent factors.
int MaxItemID [get, set]
 Maximum item ID.
int MaxUserID [get, set]
 Maximum user ID.
uint NumFactors [get, set]
 Number of latent factors per user/item.
uint NumIter [get, set]
 Number of iterations over the training data.
double Regularization [get, set]
 Regularization parameter.

Detailed Description

Weighted matrix factorization method proposed by Hu et al. and Pan et al.

We use the fast learning method proposed by Hu et al. (alternating least squares), and we use a global weight to penalize observed/unobserved values.

Literature:

This recommender does NOT support incremental updates.


Member Function Documentation

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

Add a positive feedback event.

Parameters:
user_id the user ID
item_id the item ID

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Implements IRecommender.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

override double ComputeFit (  )  [inline, virtual]

Computes the fit (optimization criterion) on the training data.

Returns:
a double representing the fit, lower is better

Implements MF.

override void Iterate (  )  [inline, virtual]

Iterate once over the data.

Implements MF.

override void LoadModel ( string  filename  )  [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements ItemRecommender.

Reimplemented in BPRMF.

virtual void Optimize ( IBooleanMatrix  data,
Matrix< double >  W,
Matrix< double >  H 
) [inline, protected, virtual]

Optimizes the specified data.

Parameters:
data data
W W
H H
override double Predict ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Predict the weight for a given user-item combination.

If the user or the item are not known to the recommender, zero 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 weight

Implements ItemRecommender.

Reimplemented in BPRMF.

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

Remove all feedback events by the given user-item combination.

Parameters:
user_id the user ID
item_id the item ID

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Remove all feedback by one item.

Parameters:
item_id the item ID

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Remove all feedback by one user.

Parameters:
user_id the user ID

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, and MostPopular.

override void SaveModel ( string  filename  )  [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements ItemRecommender.

Reimplemented in BPRMF.

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 ItemRecommender.


Member Data Documentation

Matrix<double> item_factors [protected, inherited]

Latent item factor matrix.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

Matrix<double> user_factors [protected, inherited]

Latent user factor matrix.


Property Documentation

double CPos [get, set]

C position: the weight/confidence that is put on positive observations.

The alpha value in Hu et al.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

double InitMean [get, set, inherited]

Mean of the normal distribution used to initialize the latent factors.

double InitStdDev [get, set, inherited]

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

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

uint NumFactors [get, set, inherited]

Number of latent factors per user/item.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

double Regularization [get, set]

Regularization parameter.


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