MyMediaLite  3.04
Public Member Functions | Protected Member Functions | Properties
MostPopular Class Reference

Most-popular item recommender. More...

Inheritance diagram for MostPopular:
IncrementalItemRecommender ItemRecommender IIncrementalItemRecommender Recommender IIncrementalRecommender IRecommender

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
override void LoadModel (string filename)
 Get the model parameters from a file.
 MostPopular ()
 Default constructor.
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.
override void RemoveItem (int item_id)
 Remove all feedback by one item.
override void RemoveUser (int user_id)
 Remove all feedback by one user.
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 Member Functions

override void AddItem (int item_id)
virtual void AddUser (int user_id)

Properties

bool ByUser [get, set]
 If true, the popularity of an item is measured by the number of unique users that have accessed it. If false, the popularity is measured by the number of accesses to the item.
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.
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

Detailed Description

Most-popular item recommender.

Items are weighted by how often they have been seen in the past.

This method is not personalized.

This recommender supports incremental updates.


Constructor & Destructor Documentation

MostPopular ( ) [inline]

Default constructor.


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

Object Clone ( ) [inline, inherited]

create a shallow copy of the object

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

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, virtual]

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 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_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.

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

Remove all feedback by one item.

Parameters:
item_idthe item ID

Reimplemented from IncrementalItemRecommender.

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

Remove all feedback by one user.

Parameters:
user_idthe user ID

Reimplemented from IncrementalItemRecommender.

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

Save the model parameters to a file.

Parameters:
filenamethe name of the file to write to

Reimplemented from Recommender.

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.

Reimplemented from Recommender.


Property Documentation

bool ByUser [get, set]

If true, the popularity of an item is measured by the number of unique users that have accessed it. If false, the popularity is measured by the number of accesses to the item.

virtual IPosOnlyFeedback Feedback [get, set, inherited]

the feedback data to be used for training

int MaxItemID [get, set, inherited]

Maximum item ID.

int MaxUserID [get, set, inherited]

Maximum user ID.

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.


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