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Public Member Functions | Protected Member Functions | Protected Attributes | Properties
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

List of all members.

Public Member Functions

virtual void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events 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
override float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model.
override void Iterate ()
 Iterate once over the data.
override void LoadModel (string file)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict the weight for a given user-item combination.
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)
virtual void RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations.
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< float > W, Matrix< float > H)
 Optimizes the specified data.

Protected Attributes

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

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 ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

Add positive feedback events and perform incremental training.

Parameters:
feedbackcollection of user id - item id tuples

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_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

override float ComputeObjective ( ) [inline, virtual]

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 ( ) [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:
filenamethe name of the file to read from

Reimplemented from Recommender.

Reimplemented in BPRMF.

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

Optimizes the specified data.

Parameters:
datadata
WW
HH
override float 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_idthe user ID
item_idthe item ID
Returns:
the predicted weight

Implements Recommender.

Reimplemented in BPRMF, BPRMF_ItemMapping, and BPRMF_UserMapping.

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.

virtual void RemoveFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual, inherited]

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

Parameters:
feedbackcollection of user id - item id tuples

Implements IIncrementalItemRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Remove all feedback by one item.

Parameters:
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Remove all feedback by one user.

Parameters:
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, and MostPopular.

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

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

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 Recommender.


Member Data Documentation

Matrix<float> item_factors [protected, inherited]

Latent item factor matrix.

int num_factors = 10 [protected, inherited]

Number of latent factors per user/item.

Matrix<float> 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.

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: