MyMediaLite
3.02
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Uses the average rating value of an item for prediction. More...
Public Member Functions | |
override void | AddRatings (IRatings ratings) |
Add new ratings and perform incremental training. | |
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. | |
Object | Clone () |
create a shallow copy of the object | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
override float | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination. | |
override 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 | |
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. | |
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 |
Uses the average rating value of an item for prediction.
This recommender supports incremental updates.
override void AddRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Add new ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
override bool CanPredict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
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 |
Reimplemented from RatingPredictor.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
override void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from RatingPredictor.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID |
item_id | the item ID |
Implements RatingPredictor.
override void RemoveItem | ( | int | item_id | ) | [inline, virtual] |
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 |
Reimplemented from IncrementalRatingPredictor.
override void RemoveRatings | ( | IDataSet | ratings | ) | [inline, virtual] |
Remove existing ratings and perform "incremental" training.
ratings | the 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.
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, inherited] |
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, inherited] |
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, inherited] |
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 |
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
IList<float> entity_averages [protected, inherited] |
The average rating for each entity.
float global_average [protected, inherited] |
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, inherited] |
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.