MyMediaLite
3.04
|
Social-network-aware matrix factorization. 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 | |
override float | ComputeObjective () |
Compute the regularized loss. | |
override void | Iterate () |
Run one iteration (= pass over the training data) | |
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) |
override void | RemoveItem (int item_id) |
Remove all feedback by one item. | |
override void | RemoveRatings (IDataSet ratings) |
Remove existing ratings and perform "incremental" training. | |
override void | RemoveUser (int user_id) |
Remove all feedback by one user. | |
override void | RetrainItem (int item_id) |
Updates the latent factors of an item. | |
override void | RetrainUser (int user_id) |
Updates the latent factors on a 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 | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
double | ComputeLoss () |
Computes the value of the loss function that is currently being optimized. | |
override float[] | FoldIn (IList< Tuple< int, float >> rated_items) |
Compute parameters (latent factors) for a user represented by ratings. | |
override void | InitModel () |
Initialize the model data structure. | |
override void | Iterate (IList< int > rating_indices, bool update_user, bool update_item) |
Iterate once over rating data and adjust corresponding factors (stochastic gradient descent) | |
float | Predict (int user_id, int item_id, bool bound) |
override float | Predict (float[] user_vector, int item_id) |
Predict rating for a fold-in user and an item. | |
void | SetupLoss () |
Set up the common part of the error gradient of the loss function to optimize. | |
override void | UpdateLearnRate () |
Updates current_learnrate after each epoch. | |
Protected Attributes | |
Func< double, double, float > | compute_gradient_common |
delegate to compute the common term of the error gradient | |
float | current_learnrate |
The learn rate used for the current epoch. | |
float | global_bias |
The bias (global average) | |
float[] | item_bias |
the item biases | |
Matrix< float > | item_factors |
Matrix containing the latent item factors. | |
double | last_loss = double.NegativeInfinity |
Loss for the last iteration, used by bold driver heuristics. | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
float | rating_range_size |
size of the interval of valid ratings | |
IRatings | ratings |
rating data | |
float[] | user_bias |
the user biases | |
Matrix< float > | user_factors |
Matrix containing the latent user factors. | |
Properties | |
float | BiasLearnRate [get, set] |
Learn rate factor for the bias terms. | |
float | BiasReg [get, set] |
regularization factor for the bias terms | |
bool | BoldDriver [get, set] |
Use bold driver heuristics for learning rate adaption. | |
bool | FrequencyRegularization [get, set] |
Regularization based on rating frequency. | |
double | InitMean [get, set] |
Mean of the normal distribution used to initialize the factors. | |
double | InitStdDev [get, set] |
Standard deviation of the normal distribution used to initialize the factors. | |
float | LearnRate [get, set] |
Learn rate (update step size) | |
float | LearnRateDecay [get, set] |
Multiplicative learn rate decay. | |
OptimizationTarget | Loss [get, set] |
The optimization target. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
virtual float | MaxRating [get, set] |
Maximum rating value. | |
int | MaxThreads [get, set] |
the maximum number of threads to use | |
int | MaxUserID [get, set] |
Maximum user ID. | |
virtual float | MinRating [get, set] |
Minimum rating value. | |
bool | NaiveParallelization [get, set] |
Use 'naive' parallelization strategy instead of conflict-free 'distributed' SGD. | |
uint | NumFactors [get, set] |
Number of latent factors. | |
uint | NumIter [get, set] |
Number of iterations over the training data. | |
int | NumUsers [get] |
the number of users | |
virtual IRatings | Ratings [get, set] |
The rating data. | |
float | RegI [get, set] |
regularization constant for the item factors | |
float | RegU [get, set] |
regularization constant for the user factors | |
override float | Regularization [set] |
Regularization parameter. | |
float | SocialRegularization [get, set] |
Social network regularization constant. | |
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 | UserRelation [get, set] |
Social-network-aware matrix factorization.
This implementation expects a binary trust network.
Literature:
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
double ComputeLoss | ( | ) | [inline, protected, inherited] |
Computes the value of the loss function that is currently being optimized.
override float ComputeObjective | ( | ) | [inline, virtual] |
Compute the regularized loss.
Reimplemented from BiasedMatrixFactorization.
override float [] FoldIn | ( | IList< Tuple< int, float >> | rated_items | ) | [inline, protected, virtual, inherited] |
Compute parameters (latent factors) for a user represented by ratings.
rated_items | a list of (item ID, rating value) pairs |
Reimplemented from MatrixFactorization.
Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, and SigmoidItemAsymmetricFactorModel.
override void InitModel | ( | ) | [inline, protected, virtual] |
Initialize the model data structure.
Reimplemented from BiasedMatrixFactorization.
override void Iterate | ( | IList< int > | rating_indices, |
bool | update_user, | ||
bool | update_item | ||
) | [inline, protected, virtual] |
Iterate once over rating data and adjust corresponding factors (stochastic gradient descent)
rating_indices | a list of indices pointing to the ratings to iterate over |
update_user | true if user factors to be updated |
update_item | true if item factors to be updated |
Reimplemented from BiasedMatrixFactorization.
override void Iterate | ( | ) | [inline, virtual, inherited] |
Run one iteration (= pass over the training data)
Reimplemented from MatrixFactorization.
override void LoadModel | ( | string | filename | ) | [inline, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from MatrixFactorization.
Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
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, the global 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 |
Reimplemented from MatrixFactorization.
Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
override float Predict | ( | float[] | user_vector, |
int | item_id | ||
) | [inline, protected, virtual, inherited] |
Predict rating for a fold-in user and an item.
user_vector | a float vector representing the user |
item_id | the item ID |
Reimplemented from MatrixFactorization.
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.
override void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Reimplemented from MatrixFactorization.
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.
override void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Reimplemented from MatrixFactorization.
override void RetrainItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Updates the latent factors of an item.
item_id | the item ID |
Reimplemented from MatrixFactorization.
override void RetrainUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Updates the latent factors on a user.
user_id | the user ID |
Reimplemented from MatrixFactorization.
override void SaveModel | ( | string | filename | ) | [inline, inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from MatrixFactorization.
Reimplemented in SigmoidCombinedAsymmetricFactorModel, SigmoidItemAsymmetricFactorModel, and SigmoidUserAsymmetricFactorModel.
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.
void SetupLoss | ( | ) | [inline, protected, inherited] |
Set up the common part of the error gradient of the loss function to optimize.
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 BiasedMatrixFactorization.
override void UpdateLearnRate | ( | ) | [inline, protected, virtual, inherited] |
Updates current_learnrate after each epoch.
Reimplemented from MatrixFactorization.
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
Func<double, double, float> compute_gradient_common [protected, inherited] |
delegate to compute the common term of the error gradient
float current_learnrate [protected, inherited] |
The learn rate used for the current epoch.
float global_bias [protected, inherited] |
The bias (global average)
float [] item_bias [protected, inherited] |
the item biases
Matrix<float> item_factors [protected, inherited] |
Matrix containing the latent item factors.
double last_loss = double.NegativeInfinity [protected, inherited] |
Loss for the last iteration, used by bold driver heuristics.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
float rating_range_size [protected, inherited] |
size of the interval of valid ratings
float [] user_bias [protected, inherited] |
the user biases
Matrix<float> user_factors [protected, inherited] |
Matrix containing the latent user factors.
float BiasLearnRate [get, set, inherited] |
Learn rate factor for the bias terms.
float BiasReg [get, set, inherited] |
regularization factor for the bias terms
bool BoldDriver [get, set, inherited] |
Use bold driver heuristics for learning rate adaption.
Literature:
bool FrequencyRegularization [get, set, inherited] |
Regularization based on rating frequency.
Regularization proportional to the inverse of the square root of the number of ratings associated with the user or item. As described in the paper by Menon and Elkan.
double InitMean [get, set, inherited] |
Mean of the normal distribution used to initialize the factors.
double InitStdDev [get, set, inherited] |
Standard deviation of the normal distribution used to initialize the factors.
float LearnRate [get, set, inherited] |
Learn rate (update step size)
float LearnRateDecay [get, set, inherited] |
Multiplicative learn rate decay.
Applied after each epoch (= pass over the whole dataset)
OptimizationTarget Loss [get, set, inherited] |
The optimization target.
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual float MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxThreads [get, set, inherited] |
the maximum number of threads to use
For parallel learning, set this number to a multiple of the number of available cores/CPUs
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual float MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
bool NaiveParallelization [get, set, inherited] |
Use 'naive' parallelization strategy instead of conflict-free 'distributed' SGD.
The exact sequence of updates depends on the thread scheduling. If you want reproducible results, e.g. when setting --random-seed=N, do NOT set this property.
uint NumFactors [get, set, inherited] |
Number of latent factors.
uint NumIter [get, set, inherited] |
Number of iterations over the training data.
Implements IIterativeModel.
int NumUsers [get] |
the number of users
Implements IUserRelationAwareRecommender.
The rating data.
Implements IRatingPredictor.
Reimplemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, ItemKNN, and UserKNN.
float RegI [get, set, inherited] |
regularization constant for the item factors
float RegU [get, set, inherited] |
regularization constant for the user factors
override float Regularization [set, inherited] |
Regularization parameter.
Reimplemented from MatrixFactorization.
float SocialRegularization [get, set] |
Social network regularization constant.
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 UserRelation [get, set] |
The binary user relation
Implements IUserRelationAwareRecommender.