MyMediaLite
3.10
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SVD++: Matrix factorization that also takes into account what users have rated 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 | |
virtual 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 | |
virtual 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 | |
SVDPlusPlus () | |
Default constructor | |
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) |
virtual double | ComputeLoss () |
Compute the value of the loss function that is currently being optimized (RMSE) | |
override float[] | FoldIn (IList< Tuple< int, float >> rated_items) |
Compute parameters (latent factors) for a user represented by ratings | |
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) | |
void | PrecomputeFactors (int user_id) |
Precompute the factors for a given user | |
void | PrecomputeUserFactors () |
Precompute all user factors | |
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 | |
virtual void | UpdateLearnRate () |
Updates current_learnrate after each epoch |
Protected Attributes | |
float | global_bias |
The bias (global average) | |
int[][] | items_rated_by_user |
The items rated by the users | |
float | max_rating |
Maximum rating value | |
float | min_rating |
Minimum rating value | |
IRatings | ratings |
rating data | |
float[] | y_reg |
precomputed regularization terms for the y matrix |
Properties | |
IDataSet | AdditionalFeedback [get, set] |
float | BiasLearnRate [get, set] |
bias learn rate | |
float | BiasReg [get, set] |
regularization constant for biases | |
float | Decay [get, set] |
Multiplicative learn rate decay | |
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) | |
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 | NumFactors [get, set] |
Number of latent factors | |
uint | NumIter [get, set] |
Number of iterations over the training data | |
virtual IRatings | Ratings [get, set] |
The rating data | |
virtual float | Regularization [get, set] |
Regularization parameter | |
bool | UpdateItems [get, set] |
bool | UpdateUsers [get, set] |
SVD++: Matrix factorization that also takes into account what users have rated
Literature:
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inline |
Default constructor
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inlinevirtualinherited |
Add new ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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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.
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inlineinherited |
create a shallow copy of the object
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inlineprotectedvirtual |
Compute the value of the loss function that is currently being optimized (RMSE)
Reimplemented in SigmoidSVDPlusPlus.
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inlinevirtual |
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inlineprotectedvirtual |
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 GSVDPlusPlus, and SigmoidSVDPlusPlus.
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inlinevirtualinherited |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization.
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inlineprotectedvirtual |
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 MatrixFactorization.
Reimplemented in SigmoidSVDPlusPlus, and GSVDPlusPlus.
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inline |
Get the model parameters from a file
filename | the name of the file to read from |
Reimplemented from MatrixFactorization.
Reimplemented in SigmoidSVDPlusPlus.
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inlineprotected |
Precompute the factors for a given user
user_id | the ID of the user |
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inlineprotected |
Precompute all user factors
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inline |
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 GSVDPlusPlus, and SigmoidSVDPlusPlus.
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inlineprotectedvirtual |
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.
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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.
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inlinevirtualinherited |
Remove all feedback by one item
item_id | the item ID |
Reimplemented from IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
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inlinevirtualinherited |
Remove existing ratings and perform "incremental" training
ratings | the user and item IDs of the ratings to be removed |
Reimplemented from IncrementalRatingPredictor.
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inlinevirtualinherited |
Remove all feedback by one user
user_id | the user ID |
Reimplemented from IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
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inlinevirtualinherited |
Updates the latent factors of an item
item_id | the item ID |
Reimplemented in BiasedMatrixFactorization.
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inlinevirtual |
Updates the latent factors on a user
user_id | the user ID |
Reimplemented from MatrixFactorization.
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inline |
Save the model parameters to a file
filename | the name of the file to write to |
Reimplemented from MatrixFactorization.
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inlineinherited |
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.
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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 MatrixFactorization.
Reimplemented in SigmoidSVDPlusPlus.
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inline |
Learn the model parameters of the recommender from the training data
Reimplemented from MatrixFactorization.
Reimplemented in GSVDPlusPlus, and SigmoidSVDPlusPlus.
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inlineprotectedvirtualinherited |
Updates current_learnrate after each epoch
Reimplemented in BiasedMatrixFactorization.
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inlinevirtualinherited |
Update existing ratings and perform incremental training
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
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protectedinherited |
The bias (global average)
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protected |
The items rated by the users
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protectedinherited |
Maximum rating value
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protectedinherited |
Minimum rating value
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protectedinherited |
rating data
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protected |
precomputed regularization terms for the y matrix
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getset |
bias learn rate
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getset |
regularization constant for biases
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getsetinherited |
Multiplicative learn rate decay
Applied after each epoch (= pass over the whole dataset)
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getset |
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.
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getsetinherited |
Mean of the normal distribution used to initialize the factors
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getsetinherited |
Standard deviation of the normal distribution used to initialize the factors
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getsetinherited |
Learn rate (update step size)
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getsetinherited |
Maximum item ID
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getsetinherited |
Maximum rating value
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getsetinherited |
Maximum user ID
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getsetinherited |
Minimum rating value
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getsetinherited |
Number of latent factors
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getsetinherited |
Number of iterations over the training data
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getsetinherited |
Regularization parameter