MyMediaLite  3.08
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
MF Class Reference

Abstract class for matrix factorization based item predictors. More...

Inheritance diagram for MF:
IncrementalItemRecommender IIterativeModel ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender BPRMF WRMF MultiCoreBPRMF SoftMarginRankingMF WeightedBPRMF

List of all members.

Public Member Functions

override 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
abstract float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model.
abstract void Iterate ()
 Iterate once over the data.
override void LoadModel (string file)
 Get the model parameters from a file.
 MF ()
 Default constructor.
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)
override void RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations.
override void RemoveItem (int item_id)
 Remove all feedback by one item.
override 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

override void AddItem (int item_id)
override void AddUser (int user_id)
virtual void InitModel ()
abstract void RetrainItem (int item_id)
 Retrain the latent factors of a given item.
abstract void RetrainUser (int user_id)
 Retrain the latent factors of a given user.

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

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.
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

Abstract class for matrix factorization based item predictors.


Constructor & Destructor Documentation

MF ( ) [inline]

Default constructor.


Member Function Documentation

override void AddFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual]

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 
) [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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

abstract float ComputeObjective ( ) [pure virtual]

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

Returns:
the current objective; -1 if not implemented

Implements IIterativeModel.

Implemented in BPRMF, WRMF, and SoftMarginRankingMF.

abstract void Iterate ( ) [pure virtual]

Iterate once over the data.

Implements IIterativeModel.

Implemented in BPRMF, and WRMF.

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

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from Recommender.

Reimplemented in BPRMF.

override float Predict ( int  user_id,
int  item_id 
) [inline, virtual]

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.

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) [inline, virtual]

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) [inline, virtual]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Reimplemented from IncrementalItemRecommender.

Reimplemented in BPRMF.

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

Remove all feedback by one user.

Parameters:
user_idthe user ID

Reimplemented from IncrementalItemRecommender.

abstract void RetrainItem ( int  item_id) [protected, pure virtual]

Retrain the latent factors of a given item.

Parameters:
item_idthe item ID

Implemented in BPRMF, and WRMF.

abstract void RetrainUser ( int  user_id) [protected, pure virtual]

Retrain the latent factors of a given user.

Parameters:
user_idthe user ID

Implemented in BPRMF, and WRMF.

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

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, inherited]

Member Data Documentation

Matrix<float> item_factors [protected]

Latent item factor matrix.

int num_factors = 10 [protected]

Number of latent factors per user/item.

Matrix<float> user_factors [protected]

Latent user factor matrix.


Property Documentation

virtual IPosOnlyFeedback Feedback [get, set, inherited]

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, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

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.

Implements IIterativeModel.

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: