MyMediaLite  3.04
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
EntityAverage Class Reference

Abstract class that uses an average (by entity) rating value for predictions. More...

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

List of all members.

Public Member Functions

virtual void AddRatings (IRatings new_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.
abstract float Predict (int user_id, int item_id)
 Predict rating or score for a given user-item combination.
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.
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)
virtual void RemoveItem (int item_id)
 Remove all feedback by one item.
virtual void RemoveRatings (IDataSet ratings_to_delete)
 Remove existing ratings and perform "incremental" training.
virtual void RemoveUser (int user_id)
 Remove all feedback by one user.
override 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.
virtual void UpdateRatings (IRatings new_ratings)
 Update existing ratings and perform incremental training.

Protected Member Functions

virtual 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.
void Train (IList< int > entity_ids, int max_entity_id)
 Train the recommender according to the given entity type.

Protected Attributes

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

Properties

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.
virtual IRatings Ratings [get, set]
 The rating data.
float this[int index] [get]
 return the average rating for a given entity
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 that uses an average (by entity) rating value for predictions.


Member Function Documentation

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

Add new ratings and perform incremental training.

Parameters:
ratingsthe ratings

Implements IIncrementalRatingPredictor.

Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.

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, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

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

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from Recommender.

abstract float Predict ( int  user_id,
int  item_id 
) [pure virtual, inherited]
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.

virtual void RemoveItem ( int  item_id) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.

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

Remove existing ratings and perform "incremental" training.

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

Implements IIncrementalRatingPredictor.

Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, ItemAverage, and GlobalAverage.

virtual void RemoveUser ( int  user_id) [inline, virtual, inherited]

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 
) [inline, protected]

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) [inline, virtual]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

override string ToString ( ) [inline, inherited]
void Train ( IList< int >  entity_ids,
int  max_entity_id 
) [inline, protected]

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

Update existing ratings and perform incremental training.

Parameters:
ratingsthe ratings

Implements IIncrementalRatingPredictor.

Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.


Member Data Documentation

IList<float> entity_averages [protected]

The average rating for each entity.

float global_average [protected]

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

float max_rating [protected, inherited]

Maximum rating value.

float min_rating [protected, inherited]

Minimum rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

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.

virtual IRatings Ratings [get, set, inherited]

The rating data.

Implements IRatingPredictor.

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

float this[int index] [get]

return the average rating for a given entity

Parameters:
indexthe entity index
bool UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

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

Implements IIncrementalRecommender.

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 IIncrementalRecommender.


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