MyMediaLite
3.07
|
BPR-MF with attribute-to-factor mapping. More...
Public Member Functions | |
override 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 current optimization objective (usually loss plus regularization term) of the model. | |
override void | Iterate () |
Perform one iteration of stochastic gradient ascent over the training data. | |
override void | LoadModel (string file) |
Get the model parameters from a file. | |
override float | Predict (int user_id, int item_id) |
Predict the weight 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) |
override 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 file) |
Save the model parameters to a file. | |
IList< Tuple< int, float > > | ScoreItems (IList< int > accessed_items, IList< int > candidate_items) |
Score a list of items given a list of items 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. | |
Protected Member Functions | |
override void | AddItem (int item_id) |
override void | AddUser (int user_id) |
double[] | ComputeItemMappingFit () |
Compute the fit of the item mapping. | |
double[] | ComputeUserMappingFit () |
override void | InitModel () |
virtual void | IterateWithoutReplacementUniformPair () |
Iterate over the training data, uniformly sample from user-item pairs without replacement. | |
virtual void | IterateWithoutReplacementUniformPair (IList< int > indices) |
Iterate over the training data, uniformly sample from user-item pairs without replacement. | |
virtual void | IterateWithoutReplacementUniformUser () |
Iterate over the training data, uniformly sample from users without replacement. | |
virtual void | IterateWithReplacementUniformPair () |
Iterate over the training data, uniformly sample from user-item pairs with replacement. | |
virtual void | IterateWithReplacementUniformUser () |
Iterate over the training data, uniformly sample from users with replacement. | |
virtual void | RetrainItem (int item_id) |
Retrain the latent factors of a given item. | |
virtual void | RetrainUser (int user_id) |
Retrain the latent factors of a given user. | |
int | SampleItem () |
Samples an item for the mapping training. Only items that are associated with at least one user are taken into account. | |
virtual void | SampleItemPair (int user_id, out int item_id, out int other_item_id) |
Sample a pair of items, given a user. | |
virtual bool | SampleOtherItem (int user_id, int item_id, out int other_item_id) |
Sample another item, given the first one and the user. | |
virtual void | SampleTriple (out int user_id, out int item_id, out int other_item_id) |
Sample a triple for BPR learning. | |
virtual int | SampleUser () |
Uniformly sample a user that has viewed at least one and not all items. | |
int | SampleUserWithAttributes () |
Samples an user for the mapping training. Only users that are associated with at least one item, and that actually have attributes, are taken into account. | |
virtual void | UpdateFactors (int user_id, int item_id, int other_item_id, bool update_u, bool update_i, bool update_j) |
Update latent factors according to the stochastic gradient descent update rule. | |
Protected Attributes | |
IBooleanMatrix | item_attributes |
The matrix storing the item attributes. | |
float[] | item_bias |
Item bias terms. | |
float[] | item_factor_bias |
array to store the bias for each mapping | |
Matrix< float > | item_factors |
Latent item factor matrix. | |
float | learn_rate = 0.05f |
Learning rate alpha. | |
double | learn_rate_mapping = 0.01 |
The learn rate for training the mapping functions. | |
int | num_factors = 10 |
Number of latent factors per user/item. | |
int | num_init_mapping = 5 |
number of times the regression is computed (to avoid local minima) | |
int | num_iter_mapping = 10 |
number of iterations of the mapping training procedure | |
float | reg_i = 0.0025f |
Regularization parameter for positive item factors. | |
float | reg_j = 0.00025f |
Regularization parameter for negative item factors. | |
double | reg_mapping = 0.1 |
regularization constant for the mapping | |
float | reg_u = 0.0025f |
Regularization parameter for user factors. | |
bool | update_j = true |
If set (default), update factors for negative sampled items during learning. | |
IBooleanMatrix | user_attributes |
The matrix storing the user attributes. | |
Matrix< float > | user_factors |
Latent user factor matrix. | |
Static Protected Attributes | |
static System.Random | random |
Reference to (per-thread) singleton random number generator. | |
Properties | |
float | BiasReg [get, set] |
Regularization parameter for the bias term. | |
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. | |
IBooleanMatrix | ItemAttributes [get, set] |
float | LearnRate [get, set] |
Learning rate alpha. | |
double | LearnRateMapping [get, set] |
The learn rate for training the mapping functions. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxUserID [get, set] |
Maximum user ID. | |
uint | NumFactors [get, set] |
Number of latent factors per user/item. | |
int | NumInitMapping [get, set] |
number of times the regression is computed (to avoid local minima) | |
int | NumItemAttributes [get, set] |
uint | NumIter [get, set] |
Number of iterations over the training data. | |
int | NumIterMapping [get, set] |
number of iterations of the mapping training procedure | |
int | NumUserAttributes [get, set] |
float | RegI [get, set] |
Regularization parameter for positive item factors. | |
float | RegJ [get, set] |
Regularization parameter for negative item factors. | |
double | RegMapping [get, set] |
regularization constant for the mapping | |
float | RegU [get, set] |
Regularization parameter for user factors. | |
bool | UniformUserSampling [get, set] |
Sample uniformly from users. | |
bool | UpdateItems [get, set] |
true if items shall be updated when doing incremental updates | |
bool | UpdateJ [get, set] |
If set (default), update factors for negative sampled items during learning. | |
bool | UpdateUsers [get, set] |
true if users shall be updated when doing incremental updates | |
IBooleanMatrix | UserAttributes [get, set] |
bool | WithReplacement [get, set] |
Sample positive observations with (true) or without (false) replacement. |
BPR-MF with attribute-to-factor mapping.
Literature:
override 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 |
Reimplemented from IncrementalItemRecommender.
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
double [] ComputeItemMappingFit | ( | ) | [inline, protected] |
Compute the fit of the item mapping.
override float ComputeObjective | ( | ) | [inline, virtual, inherited] |
Compute the current optimization objective (usually loss plus regularization term) of the model.
Implements MF.
Reimplemented in SoftMarginRankingMF.
override void Iterate | ( | ) | [inline, virtual] |
Perform one iteration of stochastic gradient ascent over the training data.
One iteration is samples number of positive entries in the training matrix times
Reimplemented from BPRMF.
virtual void IterateWithoutReplacementUniformPair | ( | ) | [inline, protected, virtual, inherited] |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
Reimplemented in MultiCoreBPRMF.
virtual void IterateWithoutReplacementUniformPair | ( | IList< int > | indices | ) | [inline, protected, virtual, inherited] |
Iterate over the training data, uniformly sample from user-item pairs without replacement.
virtual void IterateWithoutReplacementUniformUser | ( | ) | [inline, protected, virtual, inherited] |
Iterate over the training data, uniformly sample from users without replacement.
virtual void IterateWithReplacementUniformPair | ( | ) | [inline, protected, virtual, inherited] |
Iterate over the training data, uniformly sample from user-item pairs with replacement.
virtual void IterateWithReplacementUniformUser | ( | ) | [inline, protected, virtual, inherited] |
Iterate over the training data, uniformly sample from users with replacement.
override void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
filename | the name of the file to read from |
Reimplemented from MF.
override float Predict | ( | int | user_id, |
int | item_id | ||
) | [inline, virtual, inherited] |
Predict the weight for a given user-item combination.
If the user or the item are not known to the recommender, zero 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 MF.
Reimplemented in BPRMF_ItemMapping, and BPRMF_UserMapping.
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 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 |
Reimplemented from IncrementalItemRecommender.
override void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the item ID |
Reimplemented from IncrementalItemRecommender.
override void RemoveUser | ( | int | user_id | ) | [inline, virtual, inherited] |
Remove all feedback by one user.
user_id | the user ID |
Reimplemented from IncrementalItemRecommender.
virtual void RetrainItem | ( | int | item_id | ) | [inline, protected, virtual, inherited] |
Retrain the latent factors of a given item.
item_id | the item ID |
virtual void RetrainUser | ( | int | user_id | ) | [inline, protected, virtual, inherited] |
Retrain the latent factors of a given user.
user_id | the user ID |
int SampleItem | ( | ) | [inline, protected] |
Samples an item for the mapping training. Only items that are associated with at least one user are taken into account.
virtual void SampleItemPair | ( | int | user_id, |
out int | item_id, | ||
out int | other_item_id | ||
) | [inline, protected, virtual, inherited] |
Sample a pair of items, given a user.
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
virtual bool SampleOtherItem | ( | int | user_id, |
int | item_id, | ||
out int | other_item_id | ||
) | [inline, protected, virtual, inherited] |
Sample another item, given the first one and the user.
user_id | the user ID |
item_id | the ID of the given item |
other_item_id | the ID of the other item |
virtual void SampleTriple | ( | out int | user_id, |
out int | item_id, | ||
out int | other_item_id | ||
) | [inline, protected, virtual, inherited] |
Sample a triple for BPR learning.
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
Reimplemented in WeightedBPRMF.
virtual int SampleUser | ( | ) | [inline, protected, virtual, inherited] |
Uniformly sample a user that has viewed at least one and not all items.
int SampleUserWithAttributes | ( | ) | [inline, protected] |
Samples an user for the mapping training. Only users that are associated with at least one item, and that actually have attributes, are taken into account.
override void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
filename | the name of the file to write to |
Reimplemented from MF.
IList<Tuple<int, float> > ScoreItems | ( | IList< int > | accessed_items, |
IList< int > | candidate_items | ||
) | [inline, inherited] |
Score a list of items given a list of items that represent a new user.
accessed_items | the ratings (item IDs and rating values) representing the new user |
candidate_items | the items to be rated |
Implements IFoldInItemRecommender.
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 BPRMF.
virtual void UpdateFactors | ( | int | user_id, |
int | item_id, | ||
int | other_item_id, | ||
bool | update_u, | ||
bool | update_i, | ||
bool | update_j | ||
) | [inline, protected, virtual, inherited] |
Update latent factors according to the stochastic gradient descent update rule.
user_id | the user ID |
item_id | the ID of the first item |
other_item_id | the ID of the second item |
update_u | if true, update the user latent factors |
update_i | if true, update the latent factors of the first item |
update_j | if true, update the latent factors of the second item |
Reimplemented in SoftMarginRankingMF.
IBooleanMatrix item_attributes [protected] |
The matrix storing the item attributes.
float [] item_bias [protected, inherited] |
Item bias terms.
float [] item_factor_bias [protected] |
array to store the bias for each mapping
Matrix<float> item_factors [protected, inherited] |
Latent item factor matrix.
float learn_rate = 0.05f [protected, inherited] |
Learning rate alpha.
double learn_rate_mapping = 0.01 [protected] |
The learn rate for training the mapping functions.
int num_factors = 10 [protected, inherited] |
Number of latent factors per user/item.
int num_init_mapping = 5 [protected] |
number of times the regression is computed (to avoid local minima)
may be ignored by the recommender
int num_iter_mapping = 10 [protected] |
number of iterations of the mapping training procedure
System.Random random [static, protected, inherited] |
Reference to (per-thread) singleton random number generator.
float reg_i = 0.0025f [protected, inherited] |
Regularization parameter for positive item factors.
float reg_j = 0.00025f [protected, inherited] |
Regularization parameter for negative item factors.
double reg_mapping = 0.1 [protected] |
regularization constant for the mapping
float reg_u = 0.0025f [protected, inherited] |
Regularization parameter for user factors.
bool update_j = true [protected, inherited] |
If set (default), update factors for negative sampled items during learning.
IBooleanMatrix user_attributes [protected] |
The matrix storing the user attributes.
Matrix<float> user_factors [protected, inherited] |
Latent user factor matrix.
float BiasReg [get, set, inherited] |
Regularization parameter for the bias term.
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.
IBooleanMatrix ItemAttributes [get, set] |
the binary item attributes
Implements IItemAttributeAwareRecommender.
float LearnRate [get, set, inherited] |
Learning rate alpha.
double LearnRateMapping [get, set] |
The learn rate for training the mapping functions.
int MaxItemID [get, set, inherited] |
Maximum item ID.
int MaxUserID [get, set, inherited] |
Maximum user ID.
uint NumFactors [get, set, inherited] |
Number of latent factors per user/item.
int NumInitMapping [get, set] |
number of times the regression is computed (to avoid local minima)
may be ignored by the recommender
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.
int NumIterMapping [get, set] |
number of iterations of the mapping training procedure
int NumUserAttributes [get, set] |
Number of binary user attributes
Implements IUserAttributeAwareRecommender.
float RegI [get, set, inherited] |
Regularization parameter for positive item factors.
float RegJ [get, set, inherited] |
Regularization parameter for negative item factors.
double RegMapping [get, set] |
regularization constant for the mapping
float RegU [get, set, inherited] |
Regularization parameter for user factors.
bool UniformUserSampling [get, set, inherited] |
Sample uniformly from users.
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 UpdateJ [get, set, inherited] |
If set (default), update factors for negative sampled items during learning.
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 UserAttributes [get, set] |
The binary user attributes
Implements IUserAttributeAwareRecommender.
bool WithReplacement [get, set, inherited] |
Sample positive observations with (true) or without (false) replacement.