MyMediaLite
3.04
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Weighted kNN recommender based on user attributes. More...
Public Member Functions | |
override void | AddRatings (IRatings 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 | |
IList< int > | GetMostSimilarUsers (int user_id, uint n=10) |
get the most similar users | |
float | GetUserSimilarity (int user_id1, int user_id2) |
get the similarity between two users | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item. | |
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. | |
override void | RemoveRatings (IDataSet ratings) |
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. | |
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. | |
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 | |
virtual void | AddItem (int item_id) |
override void | AddUser (int user_id) |
override IList< float > | FoldIn (IList< Tuple< int, float >> rated_items) |
Fold in one user, identified by their ratings. | |
override void | RetrainUser (int user_id) |
Retrain model for a given user. | |
Protected Attributes | |
UserItemBaseline | baseline_predictor = new UserItemBaseline() |
underlying baseline predictor | |
ICorrelationMatrix | correlation |
Correlation matrix over some kind of entity. | |
SparseBooleanMatrix | data_user |
boolean matrix indicating which user rated which item | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
IRatings | ratings |
rating data | |
Properties | |
float | Alpha [get, set] |
Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson. | |
override IBooleanMatrix | BinaryDataMatrix [get] |
Return the data matrix that can be used to compute a correlation based on binary data. | |
RatingCorrelationType | Correlation [get, set] |
The kind of correlation to use. | |
override EntityType | Entity [get] |
The entity type of the neighbors used for rating prediction. | |
uint | K [get, set] |
Number of neighbors to take into account for predictions. | |
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. | |
uint | NumIter [get, set] |
number of iterations used for training the underlying baseline predictor | |
int | NumUserAttributes [get, set] |
override IRatings | Ratings [set] |
The rating data. | |
float | RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor | |
float | RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor | |
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 | |
IBooleanMatrix | UserAttributes [get, set] |
bool | WeightedBinary [get, set] |
If set to true, give a lower weight to evidence coming from very frequent entities. |
Weighted kNN recommender based on user attributes.
This recommender supports incremental updates.
override void AddRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Add new ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
override IList<float> FoldIn | ( | IList< Tuple< int, float >> | rated_items | ) | [inline, protected, virtual] |
Fold in one user, identified by their ratings.
rated_items | the ratings to take into account |
Reimplemented from UserKNN.
IList<int> GetMostSimilarUsers | ( | int | user_id, |
uint | n = 10 |
||
) | [inline, inherited] |
get the most similar users
user_id | the ID of the user |
n | the number of similar users to return |
Implements IUserSimilarityProvider.
float GetUserSimilarity | ( | int | user_id1, |
int | user_id2 | ||
) | [inline, inherited] |
get the similarity between two users
user_id1 | the ID of the first user |
user_id2 | the ID of the second user |
Implements IUserSimilarityProvider.
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 Recommender.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, inherited] |
Predict the rating of a given user for a given item.
If the user or the item are not known to the recommender, a suitable average rating is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.
user_id | the user ID |
item_id | the item ID |
Implements IRecommender.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
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ICollection< int > | ignore_items = null , |
||
ICollection< int > | candidate_items = null |
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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.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
override 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 |
Reimplemented from IncrementalRatingPredictor.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
override void RetrainUser | ( | int | user_id | ) | [inline, protected, virtual] |
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 Recommender.
IList<Tuple<int, float> > ScoreItems | ( | IList< Tuple< int, float >> | rated_items, |
IList< int > | candidate_items | ||
) | [inline, inherited] |
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.
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.
Reimplemented from Recommender.
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
UserItemBaseline baseline_predictor = new UserItemBaseline() [protected, inherited] |
underlying baseline predictor
ICorrelationMatrix correlation [protected, inherited] |
Correlation matrix over some kind of entity.
SparseBooleanMatrix data_user [protected, inherited] |
boolean matrix indicating which user rated which item
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
float Alpha [get, set, inherited] |
Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson.
override IBooleanMatrix BinaryDataMatrix [get, protected] |
Return the data matrix that can be used to compute a correlation based on binary data.
If a purely rating-based correlation is used, this property is ignored.
Reimplemented from UserKNN.
RatingCorrelationType Correlation [get, set, inherited] |
The kind of correlation to use.
override EntityType Entity [get, protected, inherited] |
The entity type of the neighbors used for rating prediction.
Reimplemented from KNN.
uint K [get, set, inherited] |
Number of neighbors to take into account for predictions.
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.
uint NumIter [get, set, inherited] |
number of iterations used for training the underlying baseline predictor
int NumUserAttributes [get, set] |
Number of binary user attributes
Implements IUserAttributeAwareRecommender.
float RegI [get, set, inherited] |
regularization constant for the item bias of the underlying baseline predictor
float RegU [get, set, inherited] |
regularization constant for the user bias of the underlying baseline predictor
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.
IBooleanMatrix UserAttributes [get, set] |
The binary user attributes
Implements IUserAttributeAwareRecommender.
bool WeightedBinary [get, set, inherited] |
If set to true, give a lower weight to evidence coming from very frequent entities.