Item recommender that allows folding in new users More...
Public Member Functions | |
| 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 More... | |
| void | LoadModel (string filename) |
| Get the model parameters from a file More... | |
| float | Predict (int user_id, int item_id) |
| Predict rating or score for a given user-item combination More... | |
| 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 More... | |
| void | SaveModel (string filename) |
| Save the model parameters to a file More... | |
| 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 More... | |
| string | ToString () |
| Return a string representation of the recommender More... | |
| void | Train () |
| Learn the model parameters of the recommender from the training data More... | |
Detailed Description
Item recommender that allows folding in new users
The process of folding in is computing a predictive model for a new user based on their feedback and the existing recommender, without modifying the parameters of the existing recommender.
Literature:
- Badrul Sarwar and George Karypis, Joseph Konstan, John Riedl: Incremental singular value decomposition algorithms for highly scalable recommender systems. Fifth International Conference on Computer and Information Science, 2002. http://grouplens.org/papers/pdf/sarwar_SVD.pdf
Member Function Documentation
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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
Implemented in Ensemble, ExternalItemRecommender, ExternalRatingPredictor, BiPolarSlopeOne, Recommender, SlopeOne, Constant, GlobalAverage, UserAverage, ItemAverage, and Random.
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inherited |
Get the model parameters from a file
- Parameters
-
filename the name of the file to read from
Implemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, SigmoidCombinedAsymmetricFactorModel, MF, SigmoidSVDPlusPlus, BiPolarSlopeOne, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, KNN, KNN, MostPopular, NaiveBayes, SlopeOne, SLIM, MostPopularByAttributes, Recommender, EntityAverage, Ensemble, WeightedEnsemble, GlobalAverage, ExternalItemRecommender, ExternalRatingPredictor, Constant, Random, Random, and Zero.
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inherited |
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
Implemented in BPRMF, BiasedMatrixFactorization, LatentFeatureLogLinearModel, LeastSquareSLIM, MatrixFactorization, TimeAwareBaseline, FactorWiseMatrixFactorization, GSVDPlusPlus, MF, UserItemBaseline, CoClustering, NaiveBayes, SVDPlusPlus, SLIM, SigmoidCombinedAsymmetricFactorModel, MostPopularByAttributes, SigmoidSVDPlusPlus, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, Ensemble, MostPopular, BiPolarSlopeOne, ExternalItemRecommender, ExternalRatingPredictor, ItemKNN, ItemKNN, UserKNN, SlopeOne, WeightedEnsemble, Constant, UserKNN, GlobalAverage, UserAverage, ItemAverage, Recommender, Random, Random, and Zero.
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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.
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inherited |
Save the model parameters to a file
- Parameters
-
filename the name of the file to write to
Implemented in BPRMF, MatrixFactorization, BiasedMatrixFactorization, BPRSLIM, CoClustering, LeastSquareSLIM, SVDPlusPlus, UserItemBaseline, FactorWiseMatrixFactorization, BiPolarSlopeOne, SigmoidCombinedAsymmetricFactorModel, MF, NaiveBayes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SlopeOne, KNN, MostPopular, KNN, SLIM, Recommender, MostPopularByAttributes, EntityAverage, Ensemble, WeightedEnsemble, ExternalItemRecommender, ExternalRatingPredictor, GlobalAverage, Constant, Random, Random, and Zero.
| 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
- Returns
- a list of int and float pairs, representing item IDs and predicted scores
- Parameters
-
accessed_items the ratings (item IDs and rating values) representing the new user candidate_items the items to be rated
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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.
Implemented in BPRMF, BiasedMatrixFactorization, SVDPlusPlus, MatrixFactorization, SigmoidCombinedAsymmetricFactorModel, CoClustering, BPRSLIM, SigmoidItemAsymmetricFactorModel, LeastSquareSLIM, TimeAwareBaseline, SigmoidUserAsymmetricFactorModel, LatentFeatureLogLinearModel, FactorWiseMatrixFactorization, UserItemBaseline, SigmoidSVDPlusPlus, SocialMF, NaiveBayes, WRMF, KNN, KNN, MostPopular, TimeAwareBaselineWithFrequencies, SoftMarginRankingMF, Recommender, ExternalItemRecommender, ExternalRatingPredictor, WeightedBPRMF, MultiCoreBPRMF, and Constant.
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inherited |
Learn the model parameters of the recommender from the training data
Implemented in BiasedMatrixFactorization, TimeAwareBaseline, BPRMF, KNN, MatrixFactorization, KNN, LatentFeatureLogLinearModel, CoClustering, BiPolarSlopeOne, FactorWiseMatrixFactorization, Recommender, BPRSLIM, Ensemble, SlopeOne, UserItemBaseline, LeastSquareSLIM, TimeAwareBaselineWithFrequencies, SVDPlusPlus, GSVDPlusPlus, SLIM, NaiveBayes, SigmoidCombinedAsymmetricFactorModel, MF, MostPopularByAttributes, SigmoidItemAsymmetricFactorModel, SigmoidUserAsymmetricFactorModel, SigmoidSVDPlusPlus, MostPopular, ExternalItemRecommender, ExternalRatingPredictor, MultiCoreBPRMF, WeightedBPRMF, WeightedEnsemble, Constant, ItemKNN, UserKNN, GlobalAverage, UserAverage, ItemAverage, Random, Random, and Zero.
The documentation for this interface was generated from the following file:
- IFoldInItemRecommender.cs
