ItemKNNPearson Class Reference

Weighted item-based kNN with pearson correlation. More...

Inheritance diagram for ItemKNNPearson:
ItemKNN KNN IncrementalRatingPredictor RatingPredictor IIncrementalRatingPredictor IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override 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.
override double Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item.
virtual void RemoveItem (int item_id)
 Remove an item from the recommender model, and delete all ratings of this item.
override 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.
override void Train ()
 Learn the model parameters of the recommender from the training data.
override void UpdateRating (int user_id, int item_id, double rating)
 Update an existing rating and perform incremental training.

Protected Member Functions

override void AddItem (int item_id)
virtual void AddUser (int user_id)
override void RetrainItem (int item_id)
 Retrain model for a given item.

Protected Attributes

UserItemBaseline baseline_predictor = new UserItemBaseline() { RegU = 10, RegI = 5 }
 underlying baseline predictor
CorrelationMatrix correlation
 Correlation matrix over some kind of entity.
SparseBooleanMatrix data_item
 Matrix indicating which item was rated by which user.
Func< int, IList< int > > GetPositivelyCorrelatedEntities
 Get positively correlated entities.
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
float Shrinkage [get, set]
 shrinkage (regularization) parameter
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

Weighted item-based kNN with pearson correlation.

This recommender supports incremental updates.


Member Function Documentation

override 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

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, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inline, inherited]

create a shallow copy of the object

override void LoadModel ( string  filename  )  [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from KNN.

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

Predict the rating of a given user for a given item.

If the user or the item are not known to the recommender, a suitable average is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted rating

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, and MatrixFactorization.

override 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

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, and MatrixFactorization.

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

Retrain model for a given item.

Parameters:
item_id the item ID

Implements ItemKNN.

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

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 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

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

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

underlying baseline predictor

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

SparseBooleanMatrix data_item [protected, inherited]

Matrix indicating which item was rated by which user.

Func<int, IList<int> > GetPositivelyCorrelatedEntities [protected, inherited]

Get positively correlated entities.

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, inherited]

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, inherited]

The rating data.

Reimplemented from KNN.

double RegI [get, set, inherited]

regularization constant for the item bias of the underlying baseline predictor

double RegU [get, set, inherited]

regularization constant for the user bias of the underlying baseline predictor

float Shrinkage [get, set]

shrinkage (regularization) parameter

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:
Generated on Mon Nov 28 19:18:49 2011 for MyMediaLite by  doxygen 1.6.3