MyMediaLite
3.02
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Abstract class that uses an average (by entity) rating value for predictions. More...
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. | |
virtual void | RemoveItem (int item_id) |
Remove an item from the recommender model, and delete all ratings of this item. | |
virtual void | RemoveRatings (IDataSet ratings_to_delete) |
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. | |
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 |
Abstract class that uses an average (by entity) rating value for predictions.
virtual void AddRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Add new ratings and perform incremental training.
ratings | the 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.
user_id | the user ID |
item_id | the item ID |
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, virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from RatingPredictor.
abstract float Predict | ( | int | user_id, |
int | item_id | ||
) | [pure virtual, inherited] |
Predict rating or score for a given user-item combination.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
Implemented in BiasedMatrixFactorization, LatentFeatureLogLinearModel, TimeAwareBaseline, MatrixFactorization, FactorWiseMatrixFactorization, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SigmoidCombinedAsymmetricFactorModel, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, BiPolarSlopeOne, SlopeOne, ItemKNN, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, and Random.
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.
item_id | the ID of the user to be removed |
Implements IIncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
virtual void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual, inherited] |
Remove existing ratings and perform "incremental" training.
ratings | the 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 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.
user_id | the ID of the user to be removed |
Implements IIncrementalRatingPredictor.
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.
entity_id | the ID of the entity to update |
indices | list of indices to use for retraining |
override void SaveModel | ( | string | filename | ) | [inline, virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from RatingPredictor.
override string ToString | ( | ) | [inline, 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.
Implements IRecommender.
Reimplemented in BiasedMatrixFactorization, SVDPlusPlus, MatrixFactorization, CoClustering, SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, SigmoidSVDPlusPlus, UserItemBaseline, SocialMF, NaiveBayes, TimeAwareBaselineWithFrequencies, UserAttributeKNN, UserKNNCosine, Constant, UserKNNPearson, ItemAttributeKNN, ItemKNNPearson, and ItemKNNCosine.
void Train | ( | IList< int > | entity_ids, |
int | max_entity_id | ||
) | [inline, protected] |
Train the recommender according to the given entity type.
entity_ids | list of all entity IDs in the training data (per rating) |
max_entity_id | the maximum entity ID |
virtual void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Implements IIncrementalRatingPredictor.
Reimplemented in MatrixFactorization, UserItemBaseline, NaiveBayes, ItemKNN, UserKNN, UserAverage, GlobalAverage, and ItemAverage.
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.
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.
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
index | the entity index |
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.