k-nearest neighbor user-based collaborative filtering More...

Inheritance diagram for UserKNN:

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
IList< int >	GetMostSimilarUsers (int user_id, uint n=10)
	get the most similar users
float	GetUserSimilarity (int user_id1, int user_id2)
	get the similarity between two users
override void	LoadModel (string filename)
	Get the model parameters from a file.
override float	Predict (int user_id, int item_id)
	Predict rating or score 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	SaveModel (string filename)
	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 Attributes
IBinaryDataCorrelationMatrix	correlation
	Correlation matrix over some kind of entity, e.g. users or items.
uint	k = 80
	The number of neighbors to take into account for prediction.
IList< IList< int > >	nearest_neighbors
	Precomputed nearest neighbors.
Properties
float	Alpha `[get, set]`
	Alpha parameter for BidirectionalConditionalProbability.
BinaryCorrelationType	Correlation `[get, set]`
	The kind of correlation to use.
override IBooleanMatrix	DataMatrix `[get]`
	data matrix to learn the correlation from
virtual IPosOnlyFeedback	Feedback `[get, set]`
	the feedback data to be used for training
uint	K `[get, set]`
	The number of neighbors to take into account for prediction.
int	MaxItemID `[get, set]`
	Maximum item ID.
int	MaxUserID `[get, set]`
	Maximum user ID.
float	Q `[get, set]`
	Exponent to be used for transforming the neighbor's weights.
bool	Weighted `[get, set]`
	Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

Detailed Description

k-nearest neighbor user-based collaborative filtering

This recommender does NOT support incremental updates.

Member Function Documentation

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.

Parameters:

user_id	the user ID
item_id	the item ID

Returns:: true if a useful prediction can be made, false otherwise

Implements IRecommender.

Reimplemented in BiPolarSlopeOne, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

IList<int> GetMostSimilarUsers	(	int	user_id,
		uint	n = `10`
	)		`[inline]`

get the most similar users

Returns:: the users most similar to a given user

Parameters:

user_id	the ID of the user
n	the number of similar users to return

Implements IUserSimilarityProvider.

float GetUserSimilarity	(	int	user_id1,
		int	user_id2
	)		`[inline]`

get the similarity between two users

Returns:: the user similarity; higher means more similar

Parameters:

user_id1	the ID of the first user
user_id2	the ID of the second user

Implements IUserSimilarityProvider.

override void LoadModel ( string filename ) [inline, virtual, inherited]

Get the model parameters from a file.

Parameters:

filename the name of the file to read from

Reimplemented from Recommender.

override float Predict	(	int	user_id,
		int	item_id
	)		`[inline, 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 Recommender.

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.

Parameters:

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

Returns:: a sorted list of (item_id, score) tuples

Implemented in WeightedEnsemble, and Ensemble.

override void SaveModel ( string filename ) [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:

filename the name of the file to write to

Reimplemented from Recommender.

override string ToString ( ) [inline, inherited]

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 Recommender.

Member Data Documentation

IBinaryDataCorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity, e.g. users or items.

uint k = 80 [protected, inherited]

The number of neighbors to take into account for prediction.

IList<IList<int> > nearest_neighbors [protected, inherited]

Precomputed nearest neighbors.

Property Documentation

float Alpha [get, set, inherited]

Alpha parameter for BidirectionalConditionalProbability.

BinaryCorrelationType Correlation [get, set, inherited]

The kind of correlation to use.

override IBooleanMatrix DataMatrix [get, protected]

data matrix to learn the correlation from

Reimplemented from KNN.

Reimplemented in UserAttributeKNN.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

uint K [get, set, inherited]

The number of neighbors to take into account for prediction.

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

float Q [get, set, inherited]

Exponent to be used for transforming the neighbor's weights.

A value of 0 leads to counting of the relevant neighbors. 1 is the usual weighted prediction. Values greater than 1 give higher weight to higher correlated neighbors.

TODO LIT

bool Weighted [get, set, inherited]

Gets or sets a value indicating whether this MyMediaLite.ItemRecommendation.KNN is weighted.

TODO add literature reference

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

ItemRecommendation/UserKNN.cs

Public Member Functions

Protected Attributes

Properties

Detailed Description

Member Function Documentation

Member Data Documentation

Property Documentation