Matrix factorization for BPR on multiple cores. More...
Public Member Functions | |
override void | AddFeedback (int user_id, int item_id) |
Add a positive feedback event 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. | |
MultiCoreBPRMF () | |
default constructor | |
override float | Predict (int user_id, int item_id) |
Predict the weight for a given user-item combination. | |
override void | RemoveFeedback (int user_id, int item_id) |
Remove all feedback events by the given user-item combination. | |
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. | |
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) |
void | CheckSampling () |
override void | InitModel () |
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. | |
virtual void | SampleItemPair (int u, out int i, out int j) |
Sample a pair of items, given a user. | |
virtual bool | SampleOtherItem (int u, int i, out int j) |
Sample another item, given the first one and the user. | |
virtual void | SampleTriple (out int u, out int i, out int j) |
Sample a triple for BPR learning. | |
virtual int | SampleUser () |
Sample a user that has viewed at least one and not all items. | |
virtual void | UpdateFactors (int u, int i, int j, bool update_u, bool update_i, bool update_j) |
Update latent factors according to the stochastic gradient descent update rule. | |
Protected Attributes | |
bool | fast_sampling = false |
Fast, but memory-intensive sampling. | |
int | fast_sampling_memory_limit = 1024 |
Fast sampling memory limit, in MiB. | |
float[] | item_bias |
Item bias terms. | |
Matrix< float > | item_factors |
Latent item factor matrix. | |
double | last_loss = double.NegativeInfinity |
Loss for the last iteration, used by bold driver heuristics. | |
float | learn_rate = 0.05f |
Learning rate alpha. | |
int | num_factors = 10 |
Number of latent factors per user/item. | |
System.Random | random |
Random number generator. | |
float | reg_i = 0.0025f |
Regularization parameter for positive item factors. | |
float | reg_j = 0.00025f |
Regularization parameter for negative item factors. | |
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. | |
Matrix< float > | user_factors |
Latent user factor matrix. | |
IList< IList< int > > | user_neg_items |
support data structure for fast sampling | |
IList< IList< int > > | user_pos_items |
support data structure for fast sampling | |
Properties | |
float | BiasReg [get, set] |
Regularization parameter for the bias term. | |
bool | BoldDriver [get, set] |
Use bold driver heuristics for learning rate adaption. | |
int | FastSamplingMemoryLimit [get, set] |
Fast sampling memory limit, in MiB. | |
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. | |
float | LearnRate [get, set] |
Learning rate alpha. | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxThreads [get, set] |
the maximum number of threads to use | |
int | MaxUserID [get, set] |
Maximum user ID. | |
uint | NumFactors [get, set] |
Number of latent factors per user/item. | |
uint | NumIter [get, set] |
Number of iterations over the training data. | |
float | RegI [get, set] |
Regularization parameter for positive item factors. | |
float | RegJ [get, set] |
Regularization parameter for negative item factors. | |
float | RegU [get, set] |
Regularization parameter for user factors. | |
bool | UniformUserSampling [get, set] |
Sample uniformly from users. | |
bool | UpdateJ [get, set] |
If set (default), update factors for negative sampled items during learning. | |
bool | WithReplacement [get, set] |
Sample positive observations with (true) or without (false) replacement. |
Matrix factorization for BPR on multiple cores.
Literature:
This recommender supports incremental updates, however they are currently not performed on multiple cores.
MultiCoreBPRMF | ( | ) | [inline] |
default constructor
override void AddFeedback | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Add a positive feedback event and perform incremental training.
user_id | the user ID | |
item_id | the item ID |
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.
Object Clone | ( | ) | [inline, inherited] |
create a shallow copy of the object
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.
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.
override void RemoveFeedback | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual, inherited] |
Remove all feedback events by the given user-item combination.
user_id | the user ID | |
item_id | the item ID |
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 |
virtual void SampleItemPair | ( | int | u, | |
out int | i, | |||
out int | j | |||
) | [inline, protected, virtual, inherited] |
Sample a pair of items, given a user.
u | the user ID | |
i | the ID of the first item | |
j | the ID of the second item |
virtual bool SampleOtherItem | ( | int | u, | |
int | i, | |||
out int | j | |||
) | [inline, protected, virtual, inherited] |
Sample another item, given the first one and the user.
u | the user ID | |
i | the ID of the given item | |
j | the ID of the other item |
virtual void SampleTriple | ( | out int | u, | |
out int | i, | |||
out int | j | |||
) | [inline, protected, virtual, inherited] |
Sample a triple for BPR learning.
u | the user ID | |
i | the ID of the first item | |
j | the ID of the second item |
Reimplemented in WeightedBPRMF.
virtual int SampleUser | ( | ) | [inline, protected, virtual, inherited] |
Sample a user that has viewed at least one and not all items.
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.
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 | u, | |
int | i, | |||
int | j, | |||
bool | update_u, | |||
bool | update_i, | |||
bool | update_j | |||
) | [inline, protected, virtual, inherited] |
Update latent factors according to the stochastic gradient descent update rule.
u | the user ID | |
i | the ID of the first item | |
j | 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.
bool fast_sampling = false [protected, inherited] |
Fast, but memory-intensive sampling.
int fast_sampling_memory_limit = 1024 [protected, inherited] |
Fast sampling memory limit, in MiB.
float [] item_bias [protected, inherited] |
Item bias terms.
Matrix<float> item_factors [protected, inherited] |
Latent item factor matrix.
double last_loss = double.NegativeInfinity [protected, inherited] |
Loss for the last iteration, used by bold driver heuristics.
float learn_rate = 0.05f [protected, inherited] |
Learning rate alpha.
int num_factors = 10 [protected, inherited] |
Number of latent factors per user/item.
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.
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.
Matrix<float> user_factors [protected, inherited] |
Latent user factor matrix.
IList<IList<int> > user_neg_items [protected, inherited] |
support data structure for fast sampling
IList<IList<int> > user_pos_items [protected, inherited] |
support data structure for fast sampling
float BiasReg [get, set, inherited] |
Regularization parameter for the bias term.
bool BoldDriver [get, set, inherited] |
Use bold driver heuristics for learning rate adaption.
Does not work too well for BPR-MF.
Literature:
int FastSamplingMemoryLimit [get, set, inherited] |
Fast sampling memory limit, in MiB.
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.
float LearnRate [get, set, inherited] |
Learning rate alpha.
int MaxItemID [get, set, inherited] |
Maximum item ID.
int MaxThreads [get, set] |
the maximum number of threads to use
Determines the number of sections the users and items will be divided into.
int MaxUserID [get, set, inherited] |
Maximum user ID.
uint NumFactors [get, set, inherited] |
Number of latent factors per user/item.
uint NumIter [get, set, inherited] |
Number of iterations over the training data.
Implements IIterativeModel.
float RegI [get, set, inherited] |
Regularization parameter for positive item factors.
float RegJ [get, set, inherited] |
Regularization parameter for negative item factors.
float RegU [get, set, inherited] |
Regularization parameter for user factors.
bool UniformUserSampling [get, set, inherited] |
Sample uniformly from users.
bool UpdateJ [get, set, inherited] |
If set (default), update factors for negative sampled items during learning.
bool WithReplacement [get, set, inherited] |
Sample positive observations with (true) or without (false) replacement.