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 FactorWiseMatrixFactorization GlobalAverage IncrementalRatingPredictor SlopeOne ItemAverage UserAverage MatrixFactorization UserItemBaseline BiasedMatrixFactorization KNN ItemKNN UserKNN ItemAttributeKNN ItemKNNCosine ItemKNNPearson UserAttributeKNN UserKNNCosine UserKNNPearson

List of all members.

Public Member Functions

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.
 RatingPredictor ()
 Default constructor.
abstract 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.

Protected Attributes

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

Properties

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.
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

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


Constructor & Destructor Documentation

RatingPredictor (  )  [inline]

Default constructor.


Member Function Documentation

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

create a shallow copy of the object

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, FactorWiseMatrixFactorization, 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, FactorWiseMatrixFactorization, 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, FactorWiseMatrixFactorization, GlobalAverage, KNN, MatrixFactorization, SlopeOne, and UserItemBaseline.

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.

Implements IRecommender.

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


Member Data Documentation

double max_rating [protected]

Maximum rating value.

double min_rating [protected]

Minimum rating value.

IRatings ratings [protected]

rating data


Property Documentation

int MaxItemID [get, set]

Maximum item ID.

virtual double MaxRating [get, set]

Maximum rating value.

Implements IRatingPredictor.

int MaxUserID [get, set]

Maximum user ID.

virtual double MinRating [get, set]

Minimum 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 incremental updates

Default is true. Set to false if you do not want any updates to the item model parameters when doing incremental updates.

bool UpdateUsers [get, set]

true if users shall be updated when doing incremental updates

Default is true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.


The documentation for this class was generated from the following file:
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