MyMediaLite  3.07
Public Member Functions | Protected Member Functions | Protected Attributes | Properties
UserKNN Class Reference

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

Inheritance diagram for UserKNN:
KNN IUserSimilarityProvider IFoldInItemRecommender IncrementalItemRecommender IRecommender ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender UserAttributeKNN

List of all members.

Public Member Functions

override void AddFeedback (ICollection< Tuple< int, int >> feedback)
 Add positive feedback events 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
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 RemoveFeedback (ICollection< Tuple< int, int >> feedback)
 Remove all feedback events by the given user-item combinations.
virtual void RemoveItem (int item_id)
 Remove all feedback by one item.
virtual void RemoveUser (int user_id)
 Remove all feedback by one user.
override void SaveModel (string filename)
 Save the model parameters to a file.
IList< Tuple< int, float > > ScoreItems (IList< int > accessed_items, IList< int > candidate_items)
 Score a list of items given a list of items that represent a new user.
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

virtual void AddItem (int item_id)
override void AddUser (int user_id)
virtual IList< float > FoldIn (IList< int > items)
 Fold in one user, identified by their items.
void ResizeNearestNeighbors (int new_size)
 Resizes the nearest neighbors list if necessary.
void Update (ICollection< Tuple< int, int >> feedback)
 Update the correlation matrix for the given feedback.

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 UpdateItems [get, set]
 true if items shall be updated when doing incremental updates
bool UpdateUsers [get, set]
 true if users shall be updated when doing incremental updates
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 supports incremental updates for the BinaryCosine and Cooccurrence similarities.


Member Function Documentation

override void AddFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual]

Add positive feedback events and perform incremental training.

Parameters:
feedbackcollection of user id - item id tuples

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.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
true if a useful prediction can be made, false otherwise

Implements IRecommender.

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

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

virtual IList<float> FoldIn ( IList< int >  items) [inline, protected, virtual]

Fold in one user, identified by their items.

Returns:
a vector containing the similarities to all users
Parameters:
itemsthe items representing the user
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_idthe ID of the user
nthe 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_id1the ID of the first user
user_id2the ID of the second user

Implements IUserSimilarityProvider.

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

Get the model parameters from a file.

Parameters:
filenamethe name of the file to read from

Reimplemented from Recommender.

override float Predict ( int  user_id,
int  item_id 
) [inline]

Predict rating or score for a given user-item combination.

Parameters:
user_idthe user ID
item_idthe item ID
Returns:
the predicted score/rating for the given user-item combination

Implements IRecommender.

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_idthe user ID
nthe number of items to recommend, -1 for as many as possible
ignore_itemscollection if items that should not be returned; if null, use empty collection
candidate_itemsthe 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 RemoveFeedback ( ICollection< Tuple< int, int >>  feedback) [inline, virtual]

Remove all feedback events by the given user-item combinations.

Parameters:
feedbackcollection of user id - item id tuples

Reimplemented from IncrementalItemRecommender.

virtual void RemoveItem ( int  item_id) [inline, virtual, inherited]

Remove all feedback by one item.

Parameters:
item_idthe item ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.

virtual void RemoveUser ( int  user_id) [inline, virtual, inherited]

Remove all feedback by one user.

Parameters:
user_idthe user ID

Implements IIncrementalRecommender.

Reimplemented in BPRMF, BPRSLIM, LeastSquareSLIM, and MostPopular.

void ResizeNearestNeighbors ( int  new_size) [inline, protected, inherited]

Resizes the nearest neighbors list if necessary.

Parameters:
new_sizethe new size
override void SaveModel ( string  filename) [inline, virtual, inherited]

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

IList<Tuple<int, float> > ScoreItems ( IList< int >  accessed_items,
IList< int >  candidate_items 
) [inline]

Score a list of items given a list of items that represent a new user.

Returns:
a list of int and float pairs, representing item IDs and predicted scores
Parameters:
accessed_itemsthe ratings (item IDs and rating values) representing the new user
candidate_itemsthe items to be rated

Implements IFoldInItemRecommender.

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.

void Update ( ICollection< Tuple< int, int >>  feedback) [inline, protected, inherited]

Update the correlation matrix for the given feedback.

Parameters:
feedbackthe feedback (user-item tuples)

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 UpdateItems [get, set, inherited]

true if items shall be updated when doing incremental updates

Set to false if you do not want any updates to the item model parameters when doing incremental updates.

Implements IIncrementalRecommender.

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing incremental updates

Default should be true. Set to false if you do not want any updates to the user model parameters when doing incremental updates.

Implements IIncrementalRecommender.

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: