MyMediaLite  3.01
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
KNN Class Reference

Base class for rating predictors that use some kind of kNN. More...

Inheritance diagram for KNN:
IncrementalRatingPredictor RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender ItemKNN UserKNN ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson

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
override void LoadModel (string filename)
 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.
override void SaveModel (string filename)
 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

UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 }
 underlying baseline predictor
CorrelationMatrix correlation
 Correlation matrix over some kind of entity.
float max_rating
 Maximum rating value.
float min_rating
 Minimum rating value.
IRatings ratings
 rating data

Properties

uint K [get, set]
 Number of neighbors to take into account for predictions.
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 used for training the underlying baseline predictor
override IRatings Ratings [set]
 The rating data.
float RegI [get, set]
 regularization constant for the item bias of the underlying baseline predictor
float RegU [get, set]
 regularization constant for the user bias of the underlying baseline predictor
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 use some kind of kNN.

The method is described in section 2.2 of the paper below. One difference is that we support several iterations of alternating optimization, instead of just one.

Literature:

See also:
MyMediaLite.ItemRecommendation.KNN

Member Function Documentation

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

Add new ratings and perform incremental training.

Parameters:
ratingsthe ratings

Implements IIncrementalRatingPredictor.

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

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

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

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 RatingPredictor.

Reimplemented in ItemKNN.

abstract float Predict ( int  user_id,
int  item_id 
) [pure 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 IRecommender.

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

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_idthe ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.

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

Remove existing ratings and perform "incremental" training.

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

Implements IIncrementalRatingPredictor.

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

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_idthe 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:
filenamethe name of the file to write to

Reimplemented from RatingPredictor.

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

Update existing ratings and perform incremental training.

Parameters:
ratingsthe ratings

Implements IIncrementalRatingPredictor.

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


Member Data Documentation

UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 } [protected]

underlying baseline predictor

Correlation matrix over some kind of entity.

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

uint K [get, set]

Number of neighbors to take into account for predictions.

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 used for training the underlying baseline predictor

override IRatings Ratings [set]

The rating data.

Reimplemented from RatingPredictor.

Reimplemented in ItemKNN, and UserKNN.

float RegI [get, set]

regularization constant for the item bias of the underlying baseline predictor

float RegU [get, set]

regularization constant for the user bias of the underlying baseline predictor

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: