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 AddRating (int user_id, int item_id, double rating)
 Add a new rating 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 double 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 RemoveRating (int user_id, int item_id)
 Remove an existing rating 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 UpdateRating (int user_id, int item_id, double rating)
 Update an existing rating 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.
double max_rating
 Maximum rating value.
double 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 double MaxRating [get, set]
 Maximum rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 Minimum rating value.
override IRatings Ratings [set]
 The rating data.
double RegI [get, set]
 regularization constant for the item bias of the underlying baseline predictor
double 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 Yehuda Koren: Factor in the Neighbors: Scalable and Accurate Collaborative Filtering, Transactions on Knowledge Discovery from Data (TKDD), 2009.

See also:
MyMediaLite.ItemRecommendation.KNN

Member Function Documentation

virtual void AddRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Add a new rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Implements IIncrementalRatingPredictor.

Reimplemented in ItemKNN, MatrixFactorization, 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, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

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:
filename the name of the file to read from

Reimplemented from RatingPredictor.

Reimplemented in ItemKNN.

abstract double 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, FactorWiseMatrixFactorization, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, SlopeOne, TimeAwareBaseline, UserAverage, UserItemBaseline, and UserKNN.

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, and MatrixFactorization.

virtual void RemoveRating ( int  user_id,
int  item_id 
) [inline, virtual, inherited]

Remove an existing rating and perform "incremental" training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item

Implements IIncrementalRatingPredictor.

Reimplemented in ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.

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, and MatrixFactorization.

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, inherited]

Return a string representation of the recommender.

The ToString() method of recommenders should list the class name and all hyperparameters, separated by space characters.

Implements IRecommender.

Reimplemented in BiasedMatrixFactorization, CoClustering, FactorWiseMatrixFactorization, ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, TimeAwareBaseline, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserItemBaseline, UserKNNCosine, and UserKNNPearson.

virtual void UpdateRating ( int  user_id,
int  item_id,
double  rating 
) [inline, virtual, inherited]

Update an existing rating and perform incremental training.

Parameters:
user_id the ID of the user who performed the rating
item_id the ID of the rated item
rating the rating value

Implements IIncrementalRatingPredictor.

Reimplemented in ItemKNN, MatrixFactorization, UserItemBaseline, and UserKNN.


Member Data Documentation

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

underlying baseline predictor

Correlation matrix over some kind of entity.

double max_rating [protected, inherited]

Maximum rating value.

double 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 double MaxRating [get, set, inherited]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

virtual double MinRating [get, set, inherited]

Minimum rating value.

Implements IRatingPredictor.

override IRatings Ratings [set]

The rating data.

Reimplemented from RatingPredictor.

Reimplemented in ItemKNN, and UserKNN.

double RegI [get, set]

regularization constant for the item bias of the underlying baseline predictor

double 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:
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