MyMediaLite  3.11
Public Member Functions | Protected Member Functions | Protected Attributes | Properties | List of all members
ItemAverage Class Reference

Uses the average rating value of an item for prediction More...

Inheritance diagram for ItemAverage:
EntityAverage IncrementalRatingPredictor RatingPredictor IIncrementalRatingPredictor Recommender IRatingPredictor IRatingPredictor IIncrementalRecommender IRecommender IRecommender IRecommender

Public Member Functions

override void AddRatings (IRatings ratings)
 Add new ratings and perform incremental training More...
 
override 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 More...
 
Object Clone ()
 create a shallow copy of the object More...
 
override void LoadModel (string filename)
 Get the model parameters from a file More...
 
override float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination More...
 
IList< Tuple< int, float > > Recommend (int user_id, int n=-1, ICollection< int > ignore_items=null, ICollection< int > candidate_items=null)
 Recommend items for a given user More...
 
virtual System.Collections.Generic.IList< Tuple< int, float > > Recommend (int user_id, int n=-1, System.Collections.Generic.ICollection< int > ignore_items=null, System.Collections.Generic.ICollection< int > candidate_items=null)
 
override void RemoveItem (int item_id)
 Remove all feedback by one item More...
 
override void RemoveRatings (IDataSet ratings)
 Remove existing ratings and perform "incremental" training More...
 
virtual void RemoveUser (int user_id)
 Remove all feedback by one user More...
 
override void SaveModel (string filename)
 Save the model parameters to a file More...
 
override string ToString ()
 Return a string representation of the recommender More...
 
override void Train ()
 Learn the model parameters of the recommender from the training data More...
 
override void UpdateRatings (IRatings ratings)
 Update existing ratings and perform incremental training More...
 

Protected Member Functions

override void AddItem (int item_id)
 
virtual void AddUser (int user_id)
 
void Retrain (int entity_id, IList< int > indices)
 Retrain the recommender according to the given entity type More...
 
void Train (IList< int > entity_ids, int max_entity_id)
 Train the recommender according to the given entity type More...
 

Protected Attributes

IList< float > entity_averages
 The average rating for each entity More...
 
float global_average
 The global average rating (default prediction if there is no data about an entity) More...
 
float max_rating
 Maximum rating value More...
 
float min_rating
 Minimum rating value More...
 
IRatings ratings
 rating data More...
 

Properties

int MaxItemID [get, set]
 Maximum item ID More...
 
virtual float MaxRating [get, set]
 Maximum rating value More...
 
int MaxUserID [get, set]
 Maximum user ID More...
 
virtual float MinRating [get, set]
 Minimum rating value More...
 
virtual IRatings Ratings [get, set]
 The rating data More...
 
float this[int index] [get]
 return the average rating for a given entity More...
 
bool UpdateItems [get, set]
 
bool UpdateUsers [get, set]
 

Detailed Description

Uses the average rating value of an item for prediction

This recommender supports incremental updates.

Member Function Documentation

override void AddRatings ( IRatings  ratings)
inlinevirtual

Add new ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

override bool CanPredict ( int  user_id,
int  item_id 
)
inlinevirtual

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

Reimplemented from Recommender.

Object Clone ( )
inlineinherited

create a shallow copy of the object

override void LoadModel ( string  filename)
inlinevirtualinherited

Get the model parameters from a file

Parameters
filenamethe name of the file to read from

Reimplemented from Recommender.

override float Predict ( int  user_id,
int  item_id 
)
inlinevirtual

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 Recommender.

IList<Tuple<int, float> > Recommend ( int  user_id,
int  n = -1,
ICollection< int >  ignore_items = null,
ICollection< int >  candidate_items = null 
)
inherited

Recommend items for a given user

Parameters
user_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe candidate items to choose from; if null, use all items
Returns
a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void RemoveItem ( int  item_id)
inlinevirtual

Remove all feedback by one item

Parameters
item_idthe item ID

Reimplemented from IncrementalRatingPredictor.

override void RemoveRatings ( IDataSet  ratings)
inlinevirtual

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)
inlinevirtualinherited

Remove all feedback by one user

Parameters
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.

void Retrain ( int  entity_id,
IList< int >  indices 
)
inlineprotectedinherited

Retrain the recommender according to the given entity type

Parameters
entity_idthe ID of the entity to update
indiceslist of indices to use for retraining
override void SaveModel ( string  filename)
inlinevirtualinherited

Save the model parameters to a file

Parameters
filenamethe name of the file to write to

Reimplemented from Recommender.

override string ToString ( )
inlineinherited

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 Train ( )
inlinevirtual

Learn the model parameters of the recommender from the training data

Implements Recommender.

void Train ( IList< int >  entity_ids,
int  max_entity_id 
)
inlineprotectedinherited

Train the recommender according to the given entity type

Parameters
entity_idslist of all entity IDs in the training data (per rating)
max_entity_idthe maximum entity ID
override void UpdateRatings ( IRatings  ratings)
inlinevirtual

Update existing ratings and perform incremental training

Parameters
ratingsthe ratings

Reimplemented from IncrementalRatingPredictor.

Member Data Documentation

IList<float> entity_averages
protectedinherited

The average rating for each entity

float global_average
protectedinherited

The global average rating (default prediction if there is no data about an entity)

float max_rating
protectedinherited

Maximum rating value

float min_rating
protectedinherited

Minimum rating value

IRatings ratings
protectedinherited

rating data

Property Documentation

int MaxItemID
getsetinherited

Maximum item ID

virtual float MaxRating
getsetinherited

Maximum rating value

int MaxUserID
getsetinherited

Maximum user ID

virtual float MinRating
getsetinherited

Minimum rating value

virtual IRatings Ratings
getsetinherited

The rating data

float this[int index]
getinherited

return the average rating for a given entity

Parameters
indexthe entity index

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