MyMediaLite
3.07
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k-nearest neighbor (kNN) item-based collaborative filtering More...
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 | |
float | GetItemSimilarity (int item_id1, int item_id2) |
get the similarity between two items | |
IList< int > | GetMostSimilarItems (int item_id, uint n=10) |
get the most similar items | |
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. | |
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) |
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. |
k-nearest neighbor (kNN) item-based collaborative filtering
This recommender supports incremental updates for the BinaryCosine and Cooccurrence similarities.
override void AddFeedback | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, virtual] |
Add positive feedback events and perform incremental training.
feedback | collection 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.
user_id | the user ID |
item_id | the item ID |
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
float GetItemSimilarity | ( | int | item_id1, |
int | item_id2 | ||
) | [inline] |
get the similarity between two items
item_id1 | the ID of the first item |
item_id2 | the ID of the second item |
Implements IItemSimilarityProvider.
IList<int> GetMostSimilarItems | ( | int | item_id, |
uint | n = 10 |
||
) | [inline] |
get the most similar items
item_id | the ID of the item |
n | the number of similar items to return |
Implements IItemSimilarityProvider.
override void LoadModel | ( | string | filename | ) | [inline, virtual, inherited] |
Get the model parameters from a file.
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.
user_id | the user ID |
item_id | the item ID |
Implements Recommender.
IList<Tuple<int, float> > Recommend | ( | int | user_id, |
int | n = -1 , |
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ICollection< int > | ignore_items = null , |
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ICollection< int > | candidate_items = null |
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) | [inherited] |
Recommend items for a given user.
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 |
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.
feedback | collection of user id - item id tuples |
Reimplemented from IncrementalItemRecommender.
virtual void RemoveItem | ( | int | item_id | ) | [inline, virtual, inherited] |
Remove all feedback by one item.
item_id | the 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.
user_id | the 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.
new_size | the new size |
override void SaveModel | ( | string | filename | ) | [inline, virtual, inherited] |
Save the model parameters to a file.
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.
void Update | ( | ICollection< Tuple< int, int >> | feedback | ) | [inline, protected, inherited] |
Update the correlation matrix for the given feedback.
feedback | the feedback (user-item tuples) |
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.
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 ItemAttributeKNN.
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