MyMediaLite
3.07
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Item Attribute Aware SVD++: Matrix factorization that also takes into account _what_ users have rated and its attributes. 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. | |
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. | |
internal 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) | |
void | PrecomputeFactors (int user_id) |
Precompute the factors for a given user. | |
void | PrecomputeItemFactors () |
Precompute all item factors. | |
void | PrecomputeItemFactors (int item_id) |
Precompute the factors for a given item. | |
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 | |
internal float | current_learnrate |
The learn rate used for the current epoch. | |
float | global_bias |
The bias (global average) | |
internal float[] | item_bias |
rating biases of the items | |
internal Matrix< float > | item_factors |
Matrix containing the latent item factors. | |
int[][] | items_rated_by_user |
The items rated by the users. | |
float | max_rating |
Maximum rating value. | |
float | min_rating |
Minimum rating value. | |
internal Matrix< float > | p |
user factors (individual part) | |
Matrix< float > | q |
item factors (individual part) | |
IRatings | ratings |
rating data | |
internal float[] | user_bias |
rating biases of the users | |
internal Matrix< float > | user_factors |
Matrix containing the latent user factors. | |
Matrix< float > | x |
item factors (part expressed via the items attributes) | |
float[] | x_reg |
precomputed regularization terms for the x matrix | |
internal Matrix< float > | y |
user factors (part expressed via the rated items) | |
float[] | y_reg |
precomputed regularization terms for the y matrix | |
Properties | |
IDataSet | AdditionalFeedback [get, set] |
user-item combinations that are known to be queried | |
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. | |
IBooleanMatrix | ItemAttributes [get, set] |
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. | |
int | NumItemAttributes [get, set] |
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] |
true if items shall be updated when doing incremental updates | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates |
Item Attribute Aware SVD++: Matrix factorization that also takes into account _what_ users have rated and its attributes.
Literature:
Marcelo Manzato: gSVD++: supporting implicit feedback on recommender systems with metadata awareness. SAC 2013.
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 ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
virtual double ComputeLoss | ( | ) | [inline, protected, virtual, inherited] |
Compute the value of the loss function that is currently being optimized (RMSE)
Reimplemented in SigmoidSVDPlusPlus.
override float ComputeObjective | ( | ) | [inline, virtual, inherited] |
override float [] FoldIn | ( | IList< Tuple< int, float >> | rated_items | ) | [inline, protected, virtual] |
Compute parameters (latent factors) for a user represented by ratings.
rated_items | a list of (item ID, rating value) pairs |
Reimplemented from SVDPlusPlus.
internal override void InitModel | ( | ) | [inline, protected, virtual] |
Initialize the model data structure.
Reimplemented from SVDPlusPlus.
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 SVDPlusPlus.
virtual void Iterate | ( | ) | [inline, virtual, inherited] |
Run one iteration (= pass over the training data)
Implements IIterativeModel.
Reimplemented in BiasedMatrixFactorization.
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 SigmoidSVDPlusPlus.
void PrecomputeFactors | ( | int | user_id | ) | [inline, protected, inherited] |
Precompute the factors for a given user.
user_id | the ID of the user |
void PrecomputeItemFactors | ( | ) | [inline, protected] |
Precompute all item factors.
void PrecomputeItemFactors | ( | int | item_id | ) | [inline, protected] |
Precompute the factors for a given item.
item_id | the ID of the item |
void PrecomputeUserFactors | ( | ) | [inline, protected, inherited] |
Precompute all user factors.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [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 SVDPlusPlus.
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 IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
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 IncrementalRatingPredictor.
Reimplemented in BiasedMatrixFactorization.
virtual void RetrainItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Updates the latent factors of an item.
item_id | the item ID |
Reimplemented in BiasedMatrixFactorization.
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.
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.
override string ToString | ( | ) | [inline, inherited] |
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.
virtual void UpdateLearnRate | ( | ) | [inline, protected, virtual, inherited] |
Updates current_learnrate after each epoch.
Reimplemented in BiasedMatrixFactorization.
override void UpdateRatings | ( | IRatings | ratings | ) | [inline, virtual, inherited] |
Update existing ratings and perform incremental training.
ratings | the ratings |
Reimplemented from IncrementalRatingPredictor.
internal float current_learnrate [protected, inherited] |
The learn rate used for the current epoch.
float global_bias [protected, inherited] |
The bias (global average)
internal float [] item_bias [protected, inherited] |
rating biases of the items
internal Matrix<float> item_factors [protected, inherited] |
Matrix containing the latent item factors.
int [][] items_rated_by_user [protected, inherited] |
The items rated by the users.
float max_rating [protected, inherited] |
Maximum rating value.
float min_rating [protected, inherited] |
Minimum rating value.
internal Matrix<float> p [protected, inherited] |
user factors (individual part)
Matrix<float> q [protected] |
item factors (individual part)
internal float [] user_bias [protected, inherited] |
rating biases of the users
internal Matrix<float> user_factors [protected, inherited] |
Matrix containing the latent user factors.
Matrix<float> x [protected] |
item factors (part expressed via the items attributes)
float [] x_reg [protected] |
precomputed regularization terms for the x matrix
internal Matrix<float> y [protected, inherited] |
user factors (part expressed via the rated items)
float [] y_reg [protected, inherited] |
precomputed regularization terms for the y matrix
IDataSet AdditionalFeedback [get, set, inherited] |
user-item combinations that are known to be queried
Implements ITransductiveRatingPredictor.
float BiasLearnRate [get, set, inherited] |
bias learn rate
float BiasReg [get, set, inherited] |
regularization constant for biases
float Decay [get, set, inherited] |
Multiplicative learn rate decay.
Applied after each epoch (= pass over the whole dataset)
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.
IBooleanMatrix ItemAttributes [get, set] |
the binary item attributes
Implements IItemAttributeAwareRecommender.
float LearnRate [get, set, inherited] |
Learn rate (update step size)
int MaxItemID [get, set, inherited] |
Maximum item ID.
virtual float MaxRating [get, set, inherited] |
Maximum rating value.
Implements IRatingPredictor.
int MaxUserID [get, set, inherited] |
Maximum user ID.
virtual float MinRating [get, set, inherited] |
Minimum rating value.
Implements IRatingPredictor.
uint NumFactors [get, set, inherited] |
Number of latent factors.
int NumItemAttributes [get, set] |
an integer stating the number of attributes
Implements IItemAttributeAwareRecommender.
uint NumIter [get, set, inherited] |
Number of iterations over the training data.
Implements IIterativeModel.
The rating data.
Implements IRatingPredictor.
Reimplemented in KNN, FactorWiseMatrixFactorization, TimeAwareRatingPredictor, ItemKNN, and UserKNN.
virtual float Regularization [get, set, inherited] |
Regularization parameter.
Reimplemented in BiasedMatrixFactorization.
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.