MyMediaLite  3.02
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
NaiveBayes Class Reference

Attribute-aware rating predictor using Naive Bayes. More...

Inheritance diagram for NaiveBayes:
IncrementalRatingPredictor IItemAttributeAwareRecommender RatingPredictor IIncrementalRatingPredictor IRecommender IRatingPredictor IRatingPredictor IRecommender IRecommender

List of all members.

Public Member Functions

override void AddRatings (IRatings 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.
 NaiveBayes ()
 Default constructor.
override 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.
override void RemoveRatings (IDataSet ratings)
 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.
override void Train ()
 Learn the model parameters of the recommender from the training data.
override void UpdateRatings (IRatings ratings)
 Update existing ratings and perform incremental training.

Protected Member Functions

virtual void AddItem (int item_id)
override void AddUser (int user_id)

Protected Attributes

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

Properties

float AttributeSmoothing [get, set]
 Smoothing parameter for the attribute (given class/rating) probabilities.
float ClassSmoothing [get, set]
 Smoothing parameter for the class probabilities (rating priors)
SparseBooleanMatrix ItemAttributes [get, set]
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.
int NumItemAttributes [get, set]
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

Attribute-aware rating predictor using Naive Bayes.

This recommender supports incremental updates.


Constructor & Destructor Documentation

NaiveBayes ( ) [inline]

Default constructor.


Member Function Documentation

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

Add new ratings and perform incremental training.

Parameters:
ratingsthe ratings

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_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]

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Implements IRecommender.

override float Predict ( int  user_id,
int  item_id 
) [inline]

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.

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.

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

Remove existing ratings and perform "incremental" training.

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

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_idthe ID of the user to be removed

Implements IIncrementalRatingPredictor.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

override void SaveModel ( string  filename) [inline]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Implements IRecommender.

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.

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

Update existing ratings and perform incremental training.

Parameters:
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.


Member Data Documentation

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

float AttributeSmoothing [get, set]

Smoothing parameter for the attribute (given class/rating) probabilities.

float ClassSmoothing [get, set]

Smoothing parameter for the class probabilities (rating priors)

the binary item attributes

Implements IItemAttributeAwareRecommender.

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.

int NumItemAttributes [get, set]

an integer stating the number of attributes

Implements IItemAttributeAwareRecommender.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Implements IRatingPredictor.

Reimplemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, ItemKNN, and UserKNN.

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: