MyMediaLite
3.11
|
Weighted item-based kNN More...
Public Member Functions | |
override void | AddRatings (IRatings ratings) |
Add new ratings and perform incremental training More... | |
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 More... | |
Object | Clone () |
create a shallow copy of the object More... | |
float | GetItemSimilarity (int item_id1, int item_id2) |
get the similarity between two items More... | |
IList< int > | GetMostSimilarItems (int item_id, uint n=10) |
get the most similar items More... | |
override void | LoadModel (string filename) |
Get the model parameters from a file More... | |
override float | Predict (int user_id, int item_id) |
Predict the rating of a given user for a given item 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) |
Remove existing ratings and perform "incremental" training More... | |
virtual 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 ratings) |
Update existing ratings and perform incremental training More... | |
Protected Member Functions | |
override void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
virtual void | RetrainItem (int item_id) |
Retrain model for a given item More... | |
Protected Attributes | |
UserItemBaseline | baseline_predictor = new UserItemBaseline() |
underlying baseline predictor More... | |
ICorrelationMatrix | correlation_matrix |
Correlation matrix over some kind of entity More... | |
SparseBooleanMatrix | data_item |
Matrix indicating which item was rated by which user More... | |
float | max_rating |
Maximum rating value More... | |
float | min_rating |
Minimum rating value More... | |
IRatings | ratings |
rating data More... | |
Properties | |
float | Alpha [get, set] |
Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson More... | |
override IBooleanMatrix | BinaryDataMatrix [get] |
RatingCorrelationType | Correlation [get, set] |
The kind of correlation to use More... | |
override EntityType | Entity [get] |
uint | K [get, set] |
Number of neighbors to take into account for predictions More... | |
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... | |
uint | NumIter [get, set] |
number of iterations used for training the underlying baseline predictor More... | |
override IRatings | Ratings [set] |
float | RegI [get, set] |
regularization constant for the item bias of the underlying baseline predictor More... | |
float | RegU [get, set] |
regularization constant for the user bias of the underlying baseline predictor More... | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
bool | WeightedBinary [get, set] |
If set to true, give a lower weight to evidence coming from very frequent entities More... | |
Weighted item-based kNN
|
inlinevirtual |
Add new ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
|
inlinevirtualinherited |
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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
|
inlineinherited |
create a shallow copy of the object
|
inline |
get the similarity between two items
item_id1 | the ID of the first item |
item_id2 | the ID of the second item |
Implements IItemSimilarityProvider.
|
inline |
get the most similar items
item_id | the ID of the item |
n | the number of similar items to return |
Implements IItemSimilarityProvider.
|
inlinevirtualinherited |
Get the model parameters from a file
filename | the name of the file to read from |
Reimplemented from Recommender.
|
inline |
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 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.
|
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.
|
inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and ItemAverage.
|
inlinevirtual |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
|
inlinevirtualinherited |
Remove all feedback by one user
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in BiasedMatrixFactorization, MatrixFactorization, and UserAverage.
|
inlineprotectedvirtual |
|
inlinevirtualinherited |
Save the model parameters to a file
filename | the name of the file to write to |
Reimplemented from Recommender.
|
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.
|
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.
|
inlinevirtualinherited |
Learn the model parameters of the recommender from the training data
Implements Recommender.
|
inlinevirtual |
Update existing ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
|
protectedinherited |
underlying baseline predictor
|
protectedinherited |
Correlation matrix over some kind of entity
|
protected |
Matrix indicating which item was rated by which user
|
protectedinherited |
Maximum rating value
|
protectedinherited |
Minimum rating value
|
protectedinherited |
rating data
|
getsetinherited |
Alpha parameter for BidirectionalConditionalProbability, or shrinkage parameter for Pearson
|
getsetinherited |
The kind of correlation to use
|
getsetinherited |
Number of neighbors to take into account for predictions
|
getsetinherited |
Maximum item ID
|
getsetinherited |
Maximum rating value
|
getsetinherited |
Maximum user ID
|
getsetinherited |
Minimum rating value
|
getsetinherited |
number of iterations used for training the underlying baseline predictor
|
getsetinherited |
regularization constant for the item bias of the underlying baseline predictor
|
getsetinherited |
regularization constant for the user bias of the underlying baseline predictor
|
getsetinherited |
If set to true, give a lower weight to evidence coming from very frequent entities