UserItemBaseline Class Reference

baseline method for rating prediction More...

Inheritance diagram for UserItemBaseline:
IncrementalRatingPredictor IIterativeModel RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings 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
float ComputeObjective ()
 Compute the current optimization objective (usually loss plus regularization term) of the model.
void Iterate ()
 Run one iteration (= pass over the training data).
override void LoadModel (string filename)
 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.
virtual void RemoveItem (int item_id)
 Remove an item from the recommender model, and delete all ratings of this item.
override void RemoveRatings (IDataSet ratings)
 Remove existing ratings and perform "incremental" training.
virtual void RemoveUser (int user_id)
 Remove a user from the recommender model, and delete all their ratings.
virtual void RetrainItem (int item_id)
virtual void RetrainUser (int user_id)
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.
override void UpdateRatings (IRatings ratings)
 Update existing ratings and perform incremental training.
 UserItemBaseline ()
 Default constructor.

Protected Member Functions

override void AddItem (int item_id)
override void AddUser (int user_id)

Protected Attributes

float global_average
 the global rating average
float[] item_biases
 the item biases
float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data
float[] user_biases
 the user biases

Properties

int MaxItemID [get, set]
 Maximum item ID.
virtual float MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual float MinRating [get, set]
 Minimum rating value.
uint NumIter [get, set]
 Number of iterations to run the training.
virtual IRatings Ratings [get, set]
 The rating data.
float RegI [get, set]
 Regularization parameter for the item biases.
float RegU [get, set]
 Regularization parameter for the user biases.
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

baseline method for rating prediction

Uses the average rating value, plus a regularized user and item bias for prediction.

The method is described in section 2.1 of the paper below. One difference is that we support several iterations of alternating optimization, instead of just one.

Literature:

This recommender supports incremental updates.


Constructor & Destructor Documentation

UserItemBaseline (  )  [inline]

Default constructor.


Member Function Documentation

override void AddRatings ( IRatings  ratings  )  [inline, virtual]

Add new ratings and perform incremental training.

Parameters:
ratings the ratings

Reimplemented from IncrementalRatingPredictor.

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.

Reimplemented in BiPolarSlopeOne, Constant, GlobalAverage, ItemAverage, Random, SlopeOne, and UserAverage.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

float ComputeObjective (  )  [inline]

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

Returns:
the current objective; -1 if not implemented

Implements IIterativeModel.

void Iterate (  )  [inline]

Run one iteration (= pass over the training data).

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

Reimplemented from RatingPredictor.

override float 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 RatingPredictor.

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

Remove an item from the recommender model, and delete all ratings of this item.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
item_id the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, ItemAverage, and MatrixFactorization.

override void RemoveRatings ( IDataSet  ratings  )  [inline, virtual]

Remove existing ratings and perform "incremental" training.

Parameters:
ratings the user and item IDs of the ratings to be removed

Reimplemented from IncrementalRatingPredictor.

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

Remove a user from the recommender model, and delete all their ratings.

It is up to the recommender implementor whether there should be model updates after this action, both options are valid.

Parameters:
user_id the ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

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

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from RatingPredictor.

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 RatingPredictor.

override void UpdateRatings ( IRatings  ratings  )  [inline, virtual]

Update existing ratings and perform incremental training.

Parameters:
ratings the ratings

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

float global_average [protected]

the global rating average

float [] item_biases [protected]

the item biases

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data

float [] user_biases [protected]

the user biases


Property Documentation

int MaxItemID [get, set, inherited]

Maximum item ID.

virtual float MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual float MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

uint NumIter [get, set]

Number of iterations to run the training.

Implements IIterativeModel.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Reimplemented in FactorWiseMatrixFactorization, ItemKNN, KNN, TimeAwareRatingPredictor, and UserKNN.

float RegI [get, set]

Regularization parameter for the item biases.

float RegU [get, set]

Regularization parameter for the user biases.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Default should true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRatingPredictor.

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 IIncrementalRatingPredictor.


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