MyMediaLite
3.11
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Uses the average rating value of a user for predictions More...
Public Member Functions | |
override void | AddRatings (IRatings new_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) |
virtual void | RemoveItem (int item_id) |
Remove all feedback by one item More... | |
override void | RemoveRatings (IDataSet ratings_to_remove) |
Remove existing ratings and perform "incremental" training More... | |
override 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... | |
IList< Tuple< int, float > > | ScoreItems (IList< Tuple< int, float >> rated_items, IList< int > candidate_items) |
Rate a list of items given a list of ratings that represent a new user 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 new_ratings) |
Update existing ratings and perform incremental training More... | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
override 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] |
Uses the average rating value of a user for predictions
This recommender supports incremental updates.
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inlinevirtual |
Add new ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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inline |
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.
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inlineinherited |
create a shallow copy of the object
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inlinevirtualinherited |
Get the model parameters from a file
filename | the name of the file to read from |
Reimplemented from Recommender.
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inline |
Predict rating or score for a given user-item combination
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
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inherited |
Recommend items for a given user
user_id | the user ID |
n | the number of items to recommend, -1 for as many as possible |
ignore_items | collection if items that should not be returned; if null, use empty collection |
candidate_items | the candidate items to choose from; if null, use all items |
Implemented in WeightedEnsemble, and Ensemble.
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inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
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inlinevirtual |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtual |
Remove all feedback by one user
user_id | the user ID |
Reimplemented from IncrementalRatingPredictor.
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inlineprotectedinherited |
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 |
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inlinevirtualinherited |
Save the model parameters to a file
filename | the name of the file to write to |
Reimplemented from Recommender.
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inline |
Rate a list of items given a list of ratings that represent a new user
rated_items | the ratings (item IDs and rating values) representing the new user |
candidate_items | the items to be rated |
Implements IFoldInRatingPredictor.
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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.
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inline |
Learn the model parameters of the recommender from the training data
Implements IRecommender.
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inlineprotectedinherited |
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 |
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inlinevirtual |
Update existing ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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protectedinherited |
The average rating for each entity
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protectedinherited |
The global average rating (default prediction if there is no data about an entity)
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protectedinherited |
Maximum rating value
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protectedinherited |
Minimum rating value
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protectedinherited |
rating data
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum rating value
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getsetinherited |
Maximum user ID
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getsetinherited |
Minimum rating value
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getinherited |
return the average rating for a given entity
index | the entity index |