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:

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 double	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 BPR_Linear, BPRMF, KNN, MF, MostPopular, Random, and Zero.

abstract double 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 BPR_Linear, 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 BPR_Linear, 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 BPR_Linear, BPRMF, ItemAttributeKNN, ItemKNN, UserAttributeKNN, UserKNN, 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:

• ItemRecommender.cs