RatingPredictor Class Reference

Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction). More...

Inheritance diagram for RatingPredictor:
IRatingPredictor IRecommender BiPolarSlopeOne EntityAverage GlobalAverage MatrixFactorization SlopeOne UserItemBaseline ItemAverage UserAverage BiasedMatrixFactorization KNN ItemKNN UserKNN ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson

List of all members.

Public Member Functions

virtual void Add (int user_id, int item_id, double rating)
virtual void AddItem (int item_id)
virtual void AddUser (int user_id)
virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction can be made for a given user-item combination.
Object Clone ()
 create a shallow copy of the object
abstract 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)
virtual void RemoveRating (int user_id, int item_id)
virtual void RemoveUser (int user_id)
abstract void SaveModel (string filename)
 Save the model parameters to a file.
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)

Protected Member Functions

virtual void InitModel ()
 Inits the recommender model.

Protected Attributes

double max_rating
 The max rating value.
double min_rating
 The min rating value.
IRatings ratings
 rating data

Properties

int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 The max rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 The min rating value.
virtual IRatings Ratings [get, set]
 The rating data.
bool UpdateItems [get, set]
 true if items shall be updated when doing online updates
bool UpdateUsers [get, set]
 true if users shall be updated when doing online updates

Detailed Description

Abstract class for rating predictors that keep the rating data in memory for training (and possibly prediction).


Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
) [virtual]

Check whether a useful prediction can be made for a given user-item combination.

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 (  ) 

create a shallow copy of the object

virtual void InitModel (  )  [protected, virtual]

Inits the recommender model.

This method is called by the Train() method. When overriding, please call base.InitModel() to get the functions performed in the base class.

Reimplemented in BiasedMatrixFactorization, BiPolarSlopeOne, MatrixFactorization, SlopeOne, and UserItemBaseline.

abstract void LoadModel ( string  filename  )  [pure virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Implements IRecommender.

Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, EntityAverage, GlobalAverage, ItemKNN, KNN, MatrixFactorization, SlopeOne, and UserItemBaseline.

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

Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, GlobalAverage, ItemAverage, ItemKNN, MatrixFactorization, SlopeOne, UserAverage, UserItemBaseline, and UserKNN.

abstract void SaveModel ( string  filename  )  [pure virtual]

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Implements IRecommender.

Implemented in BiasedMatrixFactorization, BiPolarSlopeOne, EntityAverage, GlobalAverage, KNN, MatrixFactorization, SlopeOne, and UserItemBaseline.

string ToString (  )  [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.

Implemented in BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, MostPopular, Random, UserAttributeKNN, UserKNN, WeightedItemKNN, WeightedUserKNN, WRMF, Zero, BiasedMatrixFactorization, BiPolarSlopeOne, GlobalAverage, ItemAttributeKNN, ItemAverage, ItemKNNCosine, ItemKNNPearson, MatrixFactorization, SlopeOne, UserAttributeKNN, UserAverage, UserItemBaseline, UserKNNCosine, and UserKNNPearson.


Member Data Documentation

double max_rating [protected]

The max rating value.

double min_rating [protected]

The min rating value.

IRatings ratings [protected]

rating data


Property Documentation

int MaxItemID [get, set]

Maximum item ID.

Maximum item ID

virtual double MaxRating [get, set]

The max rating value.

The max rating value

Implements IRatingPredictor.

int MaxUserID [get, set]

Maximum user ID.

Maximum user ID

virtual double MinRating [get, set]

The min rating value.

The min rating value

Implements IRatingPredictor.

virtual IRatings Ratings [get, set]

The rating data.

Reimplemented in ItemKNN, and UserKNN.

bool UpdateItems [get, set]

true if items shall be updated when doing online updates

true if items shall be updated when doing online updates

bool UpdateUsers [get, set]

true if users shall be updated when doing online updates

true if users shall be updated when doing online updates


The documentation for this class was generated from the following file:
Generated on Tue May 24 12:44:19 2011 for MyMediaLite by  doxygen 1.6.3