MyMediaLite
3.08
|
Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model. More...
Public Member Functions | |
virtual void | AddFeedback (ICollection< Tuple< int, int >> feedback) |
Add positive feedback events 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 | |
KNN () | |
Default constructor. | |
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. | |
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 | RemoveFeedback (ICollection< Tuple< int, int >> feedback) |
Remove all feedback events by the given user-item combinations. | |
virtual void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
virtual void | RemoveUser (int user_id) |
Remove all feedback by one user. | |
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. | |
Protected Member Functions | |
virtual void | AddItem (int item_id) |
virtual void | AddUser (int user_id) |
void | ResizeNearestNeighbors (int new_size) |
Resizes the nearest neighbors list if necessary. | |
void | Update (ICollection< Tuple< int, int >> feedback) |
Update the correlation matrix for the given feedback. | |
Protected Attributes | |
IBinaryDataCorrelationMatrix | correlation_matrix |
Correlation matrix over some kind of entity, e.g. users or items. | |
uint | k = 80 |
The number of neighbors to take into account for prediction. | |
IList< IList< int > > | nearest_neighbors |
Precomputed nearest neighbors. | |
Properties | |
float | Alpha [get, set] |
Alpha parameter for BidirectionalConditionalProbability. | |
BinaryCorrelationType | Correlation [get, set] |
The kind of correlation to use. | |
abstract IBooleanMatrix | DataMatrix [get] |
data matrix to learn the correlation from | |
virtual IPosOnlyFeedback | Feedback [get, set] |
the feedback data to be used for training | |
uint | K [get, set] |
The number of neighbors to take into account for prediction. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
float | Q [get, set] |
Exponent to be used for transforming the neighbor's weights. | |
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 | |
bool | Weighted [get, set] |
Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted. |
Base class for item recommenders that use some kind of k-nearest neighbors (kNN) model.
KNN | ( | ) | [inline] |
Default constructor.
virtual void AddFeedback | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, virtual, inherited] |
Add positive feedback events and perform incremental training.
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in UserKNN, ItemKNN, MostPopular, and MF.
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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, 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 Recommender.
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 BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, BPRLinear, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
||
ICollection< int > | ignore_items = null , |
||
ICollection< int > | candidate_items = null |
||
) | [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 RemoveFeedback | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, virtual, inherited] |
Remove all feedback events by the given user-item combinations.
feedback | collection of user id - item id tuples |
Implements IIncrementalItemRecommender.
Reimplemented in UserKNN, MostPopular, ItemKNN, and MF.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Implements IIncrementalRecommender.
Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, MF, and MostPopular.
virtual void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Implements IIncrementalRecommender.
Reimplemented in LeastSquareSLIM, MF, and MostPopular.
void ResizeNearestNeighbors | ( | int | new_size | ) | [inline, protected] |
Resizes the nearest neighbors list if necessary.
new_size | the new size |
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 Recommender.
override string ToString | ( | ) | [inline] |
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.
void Update | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, protected] |
Update the correlation matrix for the given feedback.
feedback | the feedback (user-item tuples) |
IBinaryDataCorrelationMatrix correlation_matrix [protected] |
Correlation matrix over some kind of entity, e.g. users or items.
uint k = 80 [protected] |
The number of neighbors to take into account for prediction.
IList<IList<int> > nearest_neighbors [protected] |
Precomputed nearest neighbors.
float Alpha [get, set] |
Alpha parameter for BidirectionalConditionalProbability.
BinaryCorrelationType Correlation [get, set] |
The kind of correlation to use.
abstract IBooleanMatrix DataMatrix [get, protected] |
data matrix to learn the correlation from
Reimplemented in ItemKNN, UserKNN, ItemAttributeKNN, and UserAttributeKNN.
virtual IPosOnlyFeedback Feedback [get, set, inherited] |
the feedback data to be used for training
uint K [get, set] |
The number of neighbors to take into account for prediction.
int MaxItemID [get, set, inherited] |
Maximum item ID.
int MaxUserID [get, set, inherited] |
Maximum user ID.
float Q [get, set] |
Exponent to be used for transforming the neighbor's weights.
A value of 0 leads to counting of the relevant neighbors. 1 is the usual weighted prediction. Values greater than 1 give higher weight to higher correlated neighbors.
TODO LIT
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.
bool Weighted [get, set] |
Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.
TODO add literature reference