MyMediaLite
3.07
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Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for the elastic net loss. 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 | |
override float | ComputeObjective () |
Compute the regularized loss (regularized squared error on training data) | |
void | Iterate (int item_id) |
Perform one iteration of coordinate descent for a given set of item parameters over the training data. | |
override void | Iterate () |
Iterate this instance. | |
LeastSquareSLIM () | |
Default constructor. | |
override void | LoadModel (string filename) |
Get the model parameters from a file. | |
float | Predict (int user_id, int item_id, int exclude_item_id) |
Predict the specified user_id, item_id without taking exclude_item_id into consideration. This is needed for the coordinate descent update rule (equation 5 from Friedman et al. (2010)). | |
override 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. | |
override void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
override 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. | |
void | Train (int item_id) |
Learns the set of parameters for a given item. | |
Protected Member Functions | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
override void | InitModel () |
virtual void | RetrainItem (int item_id) |
Retrain the latent factors of a given item. | |
virtual void | UpdateParameters (int item_id, int other_item_id) |
Update item parameters according to the coordinate descent update rule. | |
Protected Attributes | |
Matrix< float > | item_weights |
Item weight matrix (the W matrix in the original paper) | |
ItemKNN | itemKNN |
The item KNN used in the feature selection step. | |
uint | neighbors = 50 |
How many neighbors to use in the kNN feature selection. | |
double | reg_l1 = 0.01 |
Regularization parameter for the L1 regularization term (lambda in the original paper) | |
double | reg_l2 = 0.001 |
Regularization parameter for the L2 regularization term (beta/2 in the original paper) | |
Properties | |
virtual IPosOnlyFeedback | Feedback [get, set] |
the feedback data to be used for training | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the latent factors. | |
double | InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the latent factors. | |
uint | K [get, set] |
How many neighbors to use in the kNN feature selection. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
uint | NumIter [get, set] |
Number of iterations over the training data. | |
double | RegL1 [get, set] |
Regularization parameter for the L1 regularization term (lambda in the original paper) | |
double | RegL2 [get, set] |
Regularization parameter for the L2 regularization term (beta/2 in the original paper) | |
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 |
Sparse Linear Methods (SLIM) for item prediction (ranking) optimized for the elastic net loss.
The model is learned using a coordinate descent algorithm with soft thresholding (Friedman et al. 2010).
Literature:
This recommender supports incremental updates.
LeastSquareSLIM | ( | ) | [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 BPRMF, UserKNN, ItemKNN, and MostPopular.
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 float ComputeObjective | ( | ) | [inline, virtual] |
Compute the regularized loss (regularized squared error on training data)
Implements SLIM.
void Iterate | ( | int | item_id | ) | [inline] |
Perform one iteration of coordinate descent for a given set of item parameters over the training data.
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 SLIM.
float Predict | ( | int | user_id, |
int | item_id, | ||
int | exclude_item_id | ||
) | [inline] |
Predict the specified user_id, item_id without taking exclude_item_id into consideration. This is needed for the coordinate descent update rule (equation 5 from Friedman et al. (2010)).
user_id | User_id. |
item_id | Item_id. |
exclude_item_id | Current item ID which shouldn't . |
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 |
Reimplemented from SLIM.
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 BPRMF, UserKNN, MostPopular, and ItemKNN.
override void RemoveItem | ( | int | item_id | ) | [inline, virtual] |
Remove all feedback by one item.
item_id | the item ID |
Reimplemented from IncrementalItemRecommender.
override void RemoveUser | ( | int | user_id | ) | [inline, virtual] |
Remove all feedback by one user.
user_id | the user ID |
Reimplemented from IncrementalItemRecommender.
virtual void RetrainItem | ( | int | item_id | ) | [inline, protected, virtual] |
Retrain the latent factors of a given item.
item_id | the item ID |
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 SLIM.
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 Train | ( | int | item_id | ) | [inline] |
Learns the set of parameters for a given item.
virtual void UpdateParameters | ( | int | item_id, |
int | other_item_id | ||
) | [inline, protected, virtual] |
Update item parameters according to the coordinate descent update rule.
item_id | the ID of the first item |
other_item_id | the ID of the second item |
Matrix<float> item_weights [protected, inherited] |
Item weight matrix (the W matrix in the original paper)
uint neighbors = 50 [protected] |
How many neighbors to use in the kNN feature selection.
double reg_l1 = 0.01 [protected] |
Regularization parameter for the L1 regularization term (lambda in the original paper)
double reg_l2 = 0.001 [protected] |
Regularization parameter for the L2 regularization term (beta/2 in the original paper)
virtual IPosOnlyFeedback Feedback [get, set, inherited] |
the feedback data to be used for training
double InitMean [get, set, inherited] |
Mean of the normal distribution used to initialize the latent factors.
double InitStdDev [get, set, inherited] |
Standard deviation of the normal distribution used to initialize the latent factors.
uint K [get, set] |
How many neighbors to use in the kNN feature selection.
int MaxItemID [get, set, inherited] |
Maximum item ID.
int MaxUserID [get, set, inherited] |
Maximum user ID.
uint NumIter [get, set, inherited] |
Number of iterations over the training data.
Implements IIterativeModel.
double RegL1 [get, set] |
Regularization parameter for the L1 regularization term (lambda in the original paper)
double RegL2 [get, set] |
Regularization parameter for the L2 regularization term (beta/2 in the original paper)
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.