BPR_Linear Class Reference

Linear model optimized for BPR. More...

Inheritance diagram for BPR_Linear:
ItemRecommender IItemAttributeAwareRecommender IIterativeModel IRecommender IRecommender

List of all members.

Public Member Functions

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
double ComputeFit ()
 Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.
void Iterate ()
 Perform one iteration of stochastic gradient ascent over the training data. One iteration is iteration_length * number of entries in the training matrix.
override void LoadModel (string filename)
 Get the model parameters from a file.
override double Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
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.

Protected Member Functions

void SampleItemPair (int u, out int i, out int j)
 Sample a pair of items, given a user.
void SampleTriple (out int u, out int i, out int j)
 Sample a triple for BPR learning.
int SampleUser ()
 Sample a user that has viewed at least one and not all items.
virtual void UpdateFeatures (int u, int i, int j)
 Modified feature update method that exploits attribute sparsity.

Protected Attributes

int iteration_length = 5
 One iteration is iteration_length * number of entries in the training matrix.

Properties

int FastSamplingMemoryLimit [get, set]
 Fast sampling memory limit, in MiB.
virtual IPosOnlyFeedback Feedback [get, set]
 the feedback data to be used for training
double InitMean [get, set]
 mean of the Gaussian distribution used to initialize the features
double InitStdev [get, set]
 standard deviation of the normal distribution used to initialize the features
SparseBooleanMatrix ItemAttributes [get, set]
double LearnRate [get, set]
 Learning rate alpha.
int MaxItemID [get, set]
 Maximum item ID.
int MaxUserID [get, set]
 Maximum user ID.
int NumItemAttributes [get, set]
uint NumIter [get, set]
 Number of iterations over the training data.
double Regularization [get, set]
 Regularization parameter.

Detailed Description

Linear model optimized for BPR.

Literature:

This recommender does NOT support incremental updates.


Member Function Documentation

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

double ComputeFit (  )  [inline]

Compute the fit (e.g. RMSE for rating prediction or AUC for item prediction/ranking) on the training data.

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

Implements IIterativeModel.

void Iterate (  )  [inline]

Perform one iteration of stochastic gradient ascent over the training data. One iteration is iteration_length * number of entries in the training matrix.

Implements IIterativeModel.

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

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements ItemRecommender.

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

Predict rating or score for a given user-item combination.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted score/rating for the given user-item combination

Implements ItemRecommender.

void SampleItemPair ( int  u,
out int  i,
out int  j 
) [inline, protected]

Sample a pair of items, given a user.

Parameters:
u the user ID
i the ID of the first item
j the ID of the second item
void SampleTriple ( out int  u,
out int  i,
out int  j 
) [inline, protected]

Sample a triple for BPR learning.

Parameters:
u the user ID
i the ID of the first item
j the ID of the second item
int SampleUser (  )  [inline, protected]

Sample a user that has viewed at least one and not all items.

Returns:
the user ID
override void SaveModel ( string  filename  )  [inline, virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements ItemRecommender.

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.

virtual void UpdateFeatures ( int  u,
int  i,
int  j 
) [inline, protected, virtual]

Modified feature update method that exploits attribute sparsity.


Member Data Documentation

int iteration_length = 5 [protected]

One iteration is iteration_length * number of entries in the training matrix.


Property Documentation

int FastSamplingMemoryLimit [get, set]

Fast sampling memory limit, in MiB.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

double InitMean [get, set]

mean of the Gaussian distribution used to initialize the features

double InitStdev [get, set]

standard deviation of the normal distribution used to initialize the features

SparseBooleanMatrix ItemAttributes [get, set]

the binary item attributes

Implements IItemAttributeAwareRecommender.

double LearnRate [get, set]

Learning rate alpha.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

int NumItemAttributes [get, set]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

uint NumIter [get, set]

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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