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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 Recommender IIncrementalRecommender IRecommender

Public Member Functions

override void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events and perform incremental training 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 current optimization objective (usually loss plus regularization term) of the model More...
 
override void Iterate ()
 Iterate once over the data More...
 
override void LoadModel (string file)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict the weight 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 RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations More...
 
override void RemoveItem (int item_id)
 Remove all feedback by one item More...
 
override void RemoveUser (int user_id)
 Remove all feedback by one user More...
 
override void SaveModel (string file)
 Save the model parameters to a file 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...
 

Protected Member Functions

override void AddItem (int item_id)
 
override void AddUser (int user_id)
 
virtual void InitModel ()
 
virtual void Optimize (IBooleanMatrix data, Matrix< float > W, Matrix< float > H)
 Optimizes the specified data More...
 
override void RetrainItem (int item_id)
 Retrain the latent factors of a given item More...
 
override void RetrainUser (int user_id)
 Retrain the latent factors of a given user More...
 

Protected Attributes

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

Properties

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

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, ALS), and we use a global parameter to give observed values higher weights.

Literature:

This recommender supports incremental updates.

Member Function Documentation

override void AddFeedback ( ICollection< Tuple< int, int >>  feedback)
inlinevirtualinherited

Add positive feedback events and perform incremental training

Parameters
feedbackcollection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

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

override float ComputeObjective ( )
inlinevirtual

Compute the current optimization objective (usually loss plus regularization term) of the model

Returns
the current objective; -1 if not implemented

Implements MF.

override void Iterate ( )
inlinevirtual

Iterate once over the data

Implements MF.

override void LoadModel ( string  filename)
inlinevirtualinherited

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Reimplemented from Recommender.

virtual void Optimize ( IBooleanMatrix  data,
Matrix< float >  W,
Matrix< float >  H 
)
inlineprotectedvirtual

Optimizes the specified data

Parameters
datadata
WW
HH
override float Predict ( int  user_id,
int  item_id 
)
inlinevirtualinherited

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_idthe user ID
item_idthe item ID
Returns
the predicted weight

Implements Recommender.

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 RemoveFeedback ( ICollection< Tuple< int, int >>  feedback)
inlinevirtualinherited

Remove all feedback events by the given user-item combinations

Parameters
feedbackcollection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

override void RemoveItem ( int  item_id)
inlinevirtualinherited

Remove all feedback by one item

Parameters
item_idthe item ID

Reimplemented from IncrementalItemRecommender.

override void RemoveUser ( int  user_id)
inlinevirtualinherited

Remove all feedback by one user

Parameters
user_idthe user ID

Reimplemented from IncrementalItemRecommender.

override void RetrainItem ( int  item_id)
inlineprotectedvirtual

Retrain the latent factors of a given item

Parameters
item_idthe item ID

Implements MF.

override void RetrainUser ( int  user_id)
inlineprotectedvirtual

Retrain the latent factors of a given user

Parameters
user_idthe user ID

Implements MF.

override void SaveModel ( string  filename)
inlinevirtualinherited

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Reimplemented from Recommender.

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 ( )
inlinevirtualinherited

Learn the model parameters of the recommender from the training data

Implements Recommender.

Reimplemented in MultiCoreBPRMF, and WeightedBPRMF.

Member Data Documentation

Matrix<float> item_factors
protectedinherited

Latent item factor matrix

int num_factors = 10
protectedinherited

Number of latent factors per user/item

Matrix<float> user_factors
protectedinherited

Latent user factor matrix

Property Documentation

double Alpha
getset

parameter for the weight/confidence that is put on positive observations

virtual IPosOnlyFeedback Feedback
getsetinherited

the feedback data to be used for training

double InitMean
getsetinherited

Mean of the normal distribution used to initialize the latent factors

double InitStdDev
getsetinherited

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

int MaxItemID
getsetinherited

Maximum item ID

int MaxUserID
getsetinherited

Maximum user ID

uint NumFactors
getsetinherited

Number of latent factors per user/item

uint NumIter
getsetinherited

Number of iterations over the training data

double Regularization
getset

Regularization parameter


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