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Public Member Functions | Protected Member Functions | Protected Attributes | Properties
BPRSLIM Class Reference

Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for BPR-Opt optimization criterion. More...

Inheritance diagram for BPRSLIM:
SLIM 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.
 BPRSLIM ()
 Default constructor.
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 fit (AUC on training data)
override void Iterate ()
 Perform one iteration of stochastic gradient ascent over the training data.
override void LoadModel (string file)
 Get the model parameters from a file.
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
double PredictWithDifference (int user_id, int pos_item_id, int neg_item_id)
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.
override 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

override void AddItem (int item_id)
override void AddUser (int user_id)
override void InitModel ()
virtual void RetrainItem (int item_id)
 Retrain the latent factors of a given item.
virtual void SampleItemPair (int u, out int i, out int j)
 Sample a pair of items, given a user.
virtual bool SampleOtherItem (int u, int i, out int j)
 Sample another item, given the first one and the user.
virtual void SampleTriple (out int u, out int i, out int j)
 Sample a triple for BPR learning.
virtual int SampleUser ()
 Sample a user that has viewed at least one and not all items.
virtual void UpdateFactors (int u, int i, int j, bool update_u, bool update_i, bool update_j)
 Update latent factors according to the stochastic gradient descent update rule.

Protected Attributes

Matrix< float > item_weights
 Item weight matrix (the W matrix in the original paper)
ItemKNN itemKNN
 The item KNN used in the feature selection step.
double learn_rate = 0.05
 Learning rate alpha.
System.Random random
 Random number generator.
double reg_i = 0.0025
 Regularization parameter for positive item weights.
double reg_j = 0.00025
 Regularization parameter for negative item weights.
bool update_j = true
 If set (default), update factors for negative sampled items during learning.

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.
double LearnRate [get, set]
 Learning rate alpha.
int MaxItemID [get, set]
 Maximum item ID.
int MaxUserID [get, set]
 Maximum user ID.
uint NumIter [get, set]
 Number of iterations over the training data.
double RegI [get, set]
 Regularization parameter for positive item weights.
double RegJ [get, set]
 Regularization parameter for negative item weights.
bool UniformUserSampling [get, set]
 Sample uniformly from users.
bool UpdateItems [get, set]
 true if items shall be updated when doing incremental updates
bool UpdateJ [get, set]
 If set (default), update factors for negative sampled items during learning.
bool UpdateUsers [get, set]
 true if users shall be updated when doing incremental updates
bool WithReplacement [get, set]
 Sample positive observations with (true) or without (false) replacement.

Detailed Description

Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for BPR-Opt optimization criterion.

This implementation differs from the algorithm in the original SLIM paper since the model here is optimized for BPR-Opt instead of the elastic net loss. The optmization algorithm used is the Sotchastic Gradient Ascent.

Literature:

Different sampling strategies are configurable by setting the UniformUserSampling and WithReplacement accordingly. To get the strategy from the original paper, set UniformUserSampling=false and WithReplacement=false. WithReplacement=true (default) gives you usually a slightly faster convergence, and UniformUserSampling=true (default) (approximately) optimizes the average AUC over all users.

This recommender supports incremental updates.


Constructor & Destructor Documentation

BPRSLIM ( ) [inline]

Default constructor.


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 UserKNN, ItemKNN, MostPopular, and MF.

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

override float ComputeObjective ( ) [inline, virtual]

Compute the fit (AUC on training data)

Returns:
the fit

Implements SLIM.

override void Iterate ( ) [inline, virtual]

Perform one iteration of stochastic gradient ascent over the training data.

One iteration is samples number of positive entries in the training matrix times

Implements SLIM.

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 SLIM.

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

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

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted score/rating for the given user-item combination

Implements Recommender.

Reimplemented in LeastSquareSLIM.

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 UserKNN, MostPopular, ItemKNN, and MF.

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

Remove all feedback by one item.

Parameters:
item_idthe item ID

Reimplemented from IncrementalItemRecommender.

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

Remove all feedback by one user.

Parameters:
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in LeastSquareSLIM, MF, and MostPopular.

virtual void RetrainItem ( int  item_id) [inline, protected, virtual]

Retrain the latent factors of a given item.

Parameters:
item_idthe item ID
virtual void SampleItemPair ( int  u,
out int  i,
out int  j 
) [inline, protected, virtual]

Sample a pair of items, given a user.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item
virtual bool SampleOtherItem ( int  u,
int  i,
out int  j 
) [inline, protected, virtual]

Sample another item, given the first one and the user.

Parameters:
uthe user ID
ithe ID of the given item
jthe ID of the other item
Returns:
true if the given item was already seen by user u
virtual void SampleTriple ( out int  u,
out int  i,
out int  j 
) [inline, protected, virtual]

Sample a triple for BPR learning.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item
virtual int SampleUser ( ) [inline, protected, virtual]

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:
filenamethe name of the file to write to

Reimplemented from SLIM.

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.

virtual void UpdateFactors ( int  u,
int  i,
int  j,
bool  update_u,
bool  update_i,
bool  update_j 
) [inline, protected, virtual]

Update latent factors according to the stochastic gradient descent update rule.

Parameters:
uthe user ID
ithe ID of the first item
jthe ID of the second item
update_uif true, update the user latent factors
update_iif true, update the latent factors of the first item
update_jif true, update the latent factors of the second item

Member Data Documentation

Matrix<float> item_weights [protected, inherited]

Item weight matrix (the W matrix in the original paper)

ItemKNN itemKNN [protected, inherited]

The item KNN used in the feature selection step.

double learn_rate = 0.05 [protected]

Learning rate alpha.

System.Random random [protected]

Random number generator.

double reg_i = 0.0025 [protected]

Regularization parameter for positive item weights.

double reg_j = 0.00025 [protected]

Regularization parameter for negative item weights.

bool update_j = true [protected]

If set (default), update factors for negative sampled items during learning.


Property Documentation

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.

double LearnRate [get, set]

Learning rate alpha.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

uint NumIter [get, set, inherited]

Number of iterations over the training data.

Implements IIterativeModel.

double RegI [get, set]

Regularization parameter for positive item weights.

double RegJ [get, set]

Regularization parameter for negative item weights.

bool UniformUserSampling [get, set]

Sample uniformly from users.

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 UpdateJ [get, set]

If set (default), update factors for negative sampled items during learning.

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.

bool WithReplacement [get, set]

Sample positive observations with (true) or without (false) replacement.


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