IncrementalRatingPredictor Class Reference

Base class for rating predictors that support incremental training. More...

Inheritance diagram for IncrementalRatingPredictor:
RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender Constant EntityAverage GlobalAverage KNN MatrixFactorization Random UserItemBaseline ItemAverage UserAverage ItemKNN UserKNN BiasedMatrixFactorization SVDPlusPlus ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson SigmoidItemAsymmetricFactorModel SocialMF SigmoidSVDPlusPlus

List of all members.

Public Member Functions

virtual void AddRatings (IRatings new_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
 IncrementalRatingPredictor ()
 Default constructor.
virtual void LoadModel (string file)
 Get the model parameters from a file.
abstract 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.
virtual void RemoveRatings (IDataSet ratings_to_delete)
 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 SaveModel (string file)
 Save the model parameters to a file.
override string ToString ()
 Return a string representation of the recommender.
abstract void Train ()
 Learn the model parameters of the recommender from the training data.
virtual void UpdateRatings (IRatings new_ratings)
 Update existing ratings and perform incremental training.

Protected Member Functions

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

Protected Attributes

float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

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.
virtual IRatings Ratings [get, set]
 The rating 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

Base class for rating predictors that support incremental training.


Constructor & Destructor Documentation

IncrementalRatingPredictor (  )  [inline]

Default constructor.


Member Function Documentation

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

Add new ratings and perform incremental training.

Parameters:
ratings the ratings

Implements IIncrementalRatingPredictor.

Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.

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

virtual void LoadModel ( string  filename  )  [inline, virtual, inherited]
abstract float Predict ( int  user_id,
int  item_id 
) [pure virtual, inherited]

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 IRecommender.

Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, LatentFeatureLogLinearModel, MatrixFactorization, Random, SigmoidItemAsymmetricFactorModel, SigmoidSVDPlusPlus, SlopeOne, SVDPlusPlus, TimeAwareBaseline, UserAverage, UserItemBaseline, and UserKNN.

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

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.

virtual 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

Implements IIncrementalRatingPredictor.

Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.

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

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.

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

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, CoClustering, Constant, EntityAverage, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, Random, SigmoidItemAsymmetricFactorModel, SlopeOne, SVDPlusPlus, and UserItemBaseline.

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

Update existing ratings and perform incremental training.

Parameters:
ratings the ratings

Implements IIncrementalRatingPredictor.

Reimplemented in GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, UserAverage, UserItemBaseline, and UserKNN.


Member Data Documentation

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


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.

virtual IRatings Ratings [get, set, inherited]

The rating data.

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

bool UpdateItems [get, set]

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]

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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