The base class for collaborative filtering recommender systems.
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| | GCollaborativeFilter () |
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| | GCollaborativeFilter (const GDomNode *pNode, GLearnerLoader &ll) |
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| virtual | ~GCollaborativeFilter () |
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| void | basicTest (double minMSE) |
| | Performs a basic unit test on this collaborative filter. More...
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| double | crossValidate (GMatrix &data, size_t folds, double *pOutMAE=NULL) |
| | This randomly assigns each rating to one of the folds. Then, for each fold, it calls train with a dataset that contains everything except for the ratings in that fold. It predicts values for the items in the fold, and returns the mean-squared difference between the predictions and the actual ratings. If pOutMAE is non-NULL, it will be set to the mean-absolute error. More...
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| virtual void | impute (GVec &vec, size_t dims)=0 |
| | pVec should be a vector of n real values, where n is the number of items/attributes/columns in the data that was used to train the model. to UNKNOWN_REAL_VALUE. This method will evaluate the known elements and impute (predict) values for the unknown elements. (The model should be trained before this method is called. Unlike the predict method, this method can operate on row-vectors that were not part of the training data.) More...
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| GMatrix * | precisionRecall (GMatrix &data, bool ideal=false) |
| | This divides the data into two equal-size parts. It trains on one part, and then measures the precision/recall using the other part. It returns a three-column data set with recall scores in column 0 and corresponding precision scores in column 1. The false-positive rate is in column 2. (So, if you want a precision-recall plot, just drop column 2. If you want an ROC curve, drop column 1 and swap the remaining two columns.) This method assumes the ratings range from 0 to 1, so be sure to scale the ratings to fit that range before calling this method. If ideal is true, then it will ignore your model and report the ideal results as if your model always predicted the correct rating. (This is useful because it shows the best possible results.) More...
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| virtual double | predict (size_t user, size_t item)=0 |
| | This returns a prediction for how the specified user will rate the specified item. (The model must be trained before this method is called. Also, some values for that user and item should have been included in the training set, or else this method will have no basis to make a good prediction.) More...
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| GRand & | rand () |
| | Returns a reference to the pseudo-random number generator associated with this object. More...
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| virtual GDomNode * | serialize (GDom *pDoc) const =0 |
| | Marshal this object into a DOM that can be converted to a variety of formats. (Implementations of this method should use baseDomNode.) More...
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| virtual void | train (GMatrix &data)=0 |
| | Trains this recommender system. Let R be an m-by-n sparse matrix of known ratings from m users of n items. pData should contain 3 columns, and one row for each known element in R. Column 0 in pData specifies the user index from 0 to m-1, column 1 in pData specifies the item index from 0 to n-1, and column 2 in pData specifies the rating vector for that user-item pair. All attributes in pData should be continuous. More...
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| double | trainAndTest (GMatrix &train, GMatrix &test, double *pOutMAE=NULL) |
| | This trains on the training set, and then tests on the test set. Returns the mean-squared difference between actual and target predictions. More...
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| void | trainDenseMatrix (const GMatrix &data, const GMatrix *pLabels=NULL) |
| | Train from an m-by-n dense matrix, where m is the number of users and n is the number of items. All attributes must be continuous. Missing values are indicated with UNKNOWN_REAL_VALUE. If pLabels is non-NULL, then the labels will be appended as additional items. More...
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| GMatrix* GClasses::GCollaborativeFilter::precisionRecall |
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GMatrix & |
data, |
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bool |
ideal = false |
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This divides the data into two equal-size parts. It trains on one part, and then measures the precision/recall using the other part. It returns a three-column data set with recall scores in column 0 and corresponding precision scores in column 1. The false-positive rate is in column 2. (So, if you want a precision-recall plot, just drop column 2. If you want an ROC curve, drop column 1 and swap the remaining two columns.) This method assumes the ratings range from 0 to 1, so be sure to scale the ratings to fit that range before calling this method. If ideal is true, then it will ignore your model and report the ideal results as if your model always predicted the correct rating. (This is useful because it shows the best possible results.)
| virtual void GClasses::GCollaborativeFilter::train |
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GMatrix & |
data | ) |
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pure virtual |
Trains this recommender system. Let R be an m-by-n sparse matrix of known ratings from m users of n items. pData should contain 3 columns, and one row for each known element in R. Column 0 in pData specifies the user index from 0 to m-1, column 1 in pData specifies the item index from 0 to n-1, and column 2 in pData specifies the rating vector for that user-item pair. All attributes in pData should be continuous.
Implemented in GClasses::GContentBoostedCF, GClasses::GContentBasedFilter, GClasses::GBagOfRecommenders, GClasses::GMatrixFactorization, GClasses::GDenseClusterRecommender, GClasses::GSparseClusterRecommender, GClasses::GInstanceRecommender, and GClasses::GBaselineRecommender.
