Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it. More...
Public Member Functions | |
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 | |
abstract void | LoadModel (string filename) |
Get the model parameters from a file. | |
abstract float | Predict (int user_id, int item_id) |
Predict rating or score for a given user-item combination. | |
abstract void | SaveModel (string filename) |
Save the model parameters to a file. | |
override string | ToString () |
Return a string representation of the recommender. | |
abstract void | Train () |
Learn the model parameters of the recommender from the training data. | |
Properties | |
virtual IPosOnlyFeedback | Feedback [get, set] |
the feedback data to be used for training | |
int | MaxItemID [get, set] |
Maximum item ID. | |
int | MaxUserID [get, set] |
Maximum user ID. |
Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it.
The data is stored in two sparse matrices: one user-wise (in the rows) and one item-wise.
Positive-only means we only which items a user has accessed/liked, but not which items a user does not like. If there is not data for a specific item, we do not know whether the user has just not yet accessed the item, or whether they really dislike it.
See http://recsyswiki/wiki/Item_recommendation and http://recsyswiki/wiki/Implicit_feedback
virtual bool CanPredict | ( | int | user_id, | |
int | item_id | |||
) | [inline, virtual] |
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] |
create a shallow copy of the object
abstract void LoadModel | ( | string | filename | ) | [pure virtual] |
Get the model parameters from a file.
filename | the name of the file to read from |
Implements IRecommender.
Implemented in BPRLinear, BPRMF, KNN, MF, MostPopular, Random, and Zero.
abstract float Predict | ( | int | user_id, | |
int | item_id | |||
) | [pure virtual] |
Predict rating or score for a given user-item combination.
user_id | the user ID | |
item_id | the item ID |
Implements IRecommender.
Implemented in BPRLinear, BPRMF, ItemKNN, MF, MostPopular, Random, UserKNN, WeightedItemKNN, WeightedUserKNN, and Zero.
abstract void SaveModel | ( | string | filename | ) | [pure virtual] |
Save the model parameters to a file.
filename | the name of the file to write to |
Implements IRecommender.
Implemented in BPRLinear, BPRMF, KNN, MF, MostPopular, Random, and Zero.
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.
Implements IRecommender.
Reimplemented in BPRLinear, BPRMF, ItemAttributeKNN, ItemKNN, MultiCoreBPRMF, SoftMarginRankingMF, UserAttributeKNN, UserKNN, WeightedBPRMF, WeightedItemKNN, WeightedUserKNN, and WRMF.
virtual IPosOnlyFeedback Feedback [get, set] |
the feedback data to be used for training
int MaxItemID [get, set] |
Maximum item ID.
int MaxUserID [get, set] |
Maximum user ID.