ItemKNN Class Reference

Weighted item-based kNN engine. More...

Inheritance diagram for ItemKNN:
KNN UserItemBaseline RatingPredictor IRatingPredictor IRecommender ItemAttributeKNN ItemKNNCosine ItemKNNPearson

List of all members.

Public Member Functions

override void Add (int user_id, int item_id, double rating)
override void AddItem (int item_id)
override void AddUser (int user_id)
virtual bool CanPredict (int user_id, int item_id)
 Check whether a useful prediction can be made for a given user-item combination.
Object Clone ()
 create a shallow copy of the object
override void LoadModel (string filename)
 Get the model parameters from a file.
override double Predict (int user_id, int item_id)
 Predict the rating of a given user for a given item.
virtual void RemoveItem (int item_id)
override void RemoveRating (int user_id, int item_id)
virtual void RemoveUser (int user_id)
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.
override void UpdateRating (int user_id, int item_id, double rating)

Protected Member Functions

override void InitModel ()
 Inits the recommender model.
virtual void RetrainItem (int item_id)
virtual void RetrainUser (int user_id)

Protected Attributes

CorrelationMatrix correlation
 Correlation matrix over some kind of entity.
SparseBooleanMatrix data_item
 Matrix indicating which item was rated by which user.
Func< int, IList< int > > GetPositivelyCorrelatedEntities
 Get positively correlated entities.
double max_rating
 The max rating value.
double min_rating
 The min rating value.
IRatings ratings
 rating data

Properties

uint K [get, set]
 Number of neighbors to take into account for predictions.
int MaxItemID [get, set]
 Maximum item ID.
virtual double MaxRating [get, set]
 The max rating value.
int MaxUserID [get, set]
 Maximum user ID.
virtual double MinRating [get, set]
 The min rating value.
override IRatings Ratings [set]
 The rating data.
double RegI [get, set]
 Regularization parameter for the item biases.
double RegU [get, set]
 Regularization parameter for the user biases.
bool UpdateItems [get, set]
 true if items shall be updated when doing online updates
bool UpdateUsers [get, set]
 true if users shall be updated when doing online updates

Detailed Description

Weighted item-based kNN engine.

This engine supports online updates.


Member Function Documentation

virtual bool CanPredict ( int  user_id,
int  item_id 
) [virtual, inherited]

Check whether a useful prediction can be made for a given user-item combination.

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, GlobalAverage, ItemAverage, SlopeOne, and UserAverage.

Object Clone (  )  [inherited]

create a shallow copy of the object

override void InitModel (  )  [protected, virtual, inherited]

Inits the recommender model.

This method is called by the Train() method. When overriding, please call base.InitModel() to get the functions performed in the base class.

Reimplemented from RatingPredictor.

override void LoadModel ( string  filename  )  [virtual]

Get the model parameters from a file.

Parameters:
filename the name of the file to read from

Reimplemented from KNN.

override double Predict ( int  user_id,
int  item_id 
) [virtual]

Predict the rating of a given user for a given item.

If the user or the item are not known to the engine, a suitable average is returned. To avoid this behavior for unknown entities, use CanPredict() to check before.

Parameters:
user_id the user ID
item_id the item ID
Returns:
the predicted rating

Reimplemented from UserItemBaseline.

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

Save the model parameters to a file.

Parameters:
filename the name of the file to write to

Reimplemented from UserItemBaseline.

override string ToString (  )  [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.

Implements IRecommender.

Reimplemented in ItemAttributeKNN, ItemKNNCosine, ItemKNNPearson, UserAttributeKNN, UserKNNCosine, and UserKNNPearson.


Member Data Documentation

CorrelationMatrix correlation [protected, inherited]

Correlation matrix over some kind of entity.

Matrix indicating which item was rated by which user.

Func<int, IList<int> > GetPositivelyCorrelatedEntities [protected]

Get positively correlated entities.

double max_rating [protected, inherited]

The max rating value.

double min_rating [protected, inherited]

The min rating value.

IRatings ratings [protected, inherited]

rating data


Property Documentation

uint K [get, set, inherited]

Number of neighbors to take into account for predictions.

int MaxItemID [get, set, inherited]

Maximum item ID.

Maximum item ID

virtual double MaxRating [get, set, inherited]

The max rating value.

The max rating value

Implements IRatingPredictor.

int MaxUserID [get, set, inherited]

Maximum user ID.

Maximum user ID

virtual double MinRating [get, set, inherited]

The min rating value.

The min rating value

Implements IRatingPredictor.

override IRatings Ratings [set]

The rating data.

Reimplemented from RatingPredictor.

double RegI [get, set, inherited]

Regularization parameter for the item biases.

If not set, the recommender will try to find suitable values.

double RegU [get, set, inherited]

Regularization parameter for the user biases.

If not set, the recommender will try to find suitable values.

bool UpdateItems [get, set, inherited]

true if items shall be updated when doing online updates

true if items shall be updated when doing online updates

bool UpdateUsers [get, set, inherited]

true if users shall be updated when doing online updates

true if users shall be updated when doing online updates


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