ItemRecommender Class Reference

Abstract item recommender class that loads the (positive-only implicit feedback) training data into memory and provides flexible access to it. More...

Inheritance diagram for ItemRecommender:
IRecommender BPRLinear IncrementalItemRecommender KNN Random Zero MF MostPopular ItemKNN UserKNN BPRMF WRMF ItemAttributeKNN WeightedItemKNN UserAttributeKNN WeightedUserKNN MultiCoreBPRMF SoftMarginRankingMF WeightedBPRMF

List of all members.

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.

Detailed Description

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


Member Function Documentation

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.

Parameters:
user_id the user ID
item_id the item ID
Returns:
true if a useful prediction can be made, false otherwise

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.

Parameters:
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.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted score/rating for the given user-item combination

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.

Parameters:
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.


Property Documentation

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.


The documentation for this class was generated from the following file:
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