This factors the sparse matrix of ratings, M, such that M = PQ^T where each row in P gives the principal preferences for the corresponding user, and each row in Q gives the linear combination of those preferences that map to a rating for an item. (Actually, P and Q also contain an extra column added for a bias.) This class is implemented according to the specification on page 631 in Takacs, G., Pilaszy, I., Nemeth, B., and Tikk, D. Scalable collaborative filtering approaches for large recommender systems. The Journal of Machine Learning Research, 10:623–656, 2009. ISSN 1532-4435., except with the addition of learning-rate decay and a different stopping criteria.
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| | GMatrixFactorization (size_t intrinsicDims) |
| | General-purpose constructor. More...
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| | GMatrixFactorization (const GDomNode *pNode, GLearnerLoader &ll) |
| | Deserialization constructor. More...
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| virtual | ~GMatrixFactorization () |
| | Destructor. More...
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| void | clampItemElement (size_t item, size_t attr, double val) |
| | Specify that a certain attribute of a certain item profile has a fixed value. (Values for attr are from 0 to m_intrinsicDims-1. No mechanism is provided to clamp the bias value.) More...
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| void | clampItems (const GMatrix &data, size_t offset=0) |
| | Assumes that column 0 of data is an item ID, and all other columns specify profile values to clamp beginning at the specifed profile offset. More...
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| void | clampUserElement (size_t user, size_t attr, double val) |
| | Specify that a certain attribute of a certain user profile has a fixed value. (Values for attr are from 0 to m_intrinsicDims-1. No mechanism is provided to clamp the bias value.) More...
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| void | clampUsers (const GMatrix &data, size_t offset=0) |
| | Assumes that column 0 of data is a user ID, and all other columns specify profile values to clamp beginning at the specifed profile offset. More...
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| GMatrix * | dropP () |
| | Returns the matrix of user preference vectors, and gives ownership to the caller. More...
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| GMatrix * | dropQ () |
| | Returns the matrix of item weight vectors, and gives ownership to the caller. More...
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| GMatrix * | getP () |
| | Returns the matrix of user preference vectors. More...
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| GMatrix * | getQ () |
| | Returns the matrix of item weight vectors. More...
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| virtual void | impute (GVec &vec, size_t dims) |
| | See the comment for GCollaborativeFilter::impute. More...
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| void | nonNegative () |
| | Constrain all non-bias weights to be non-negative during training. More...
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| virtual double | predict (size_t user, size_t item) |
| | See the comment for GCollaborativeFilter::predict. More...
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| virtual GDomNode * | serialize (GDom *pDoc) const |
| | See the comment for GCollaborativeFilter::serialize. More...
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| void | setDecayRate (double d) |
| | Set the rate to decay the learning rate. More...
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| void | setMinIters (size_t i) |
| | Set the min number of iterations to train. More...
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| void | setRegularizer (double d) |
| | Set the regularization value. More...
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| virtual void | train (GMatrix &data) |
| | See the comment for GCollaborativeFilter::train. More...
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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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| 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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| GRand & | rand () |
| | Returns a reference to the pseudo-random number generator associated with this object. 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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Public Member Functions inherited from GClasses::GCollaborativeFilter