This class makes recommendations by finding the nearest-neighbors (as determined by evaluating only overlapping ratings), and assuming that the ratings of these neighbors will be predictive of your ratings.
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| | GInstanceRecommender (size_t neighbors) |
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| | GInstanceRecommender (const GDomNode *pNode, GLearnerLoader &ll) |
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| virtual | ~GInstanceRecommender () |
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| void | clearUserDEPQ () |
| | This method clears the priority queue that keeps track of the neighbors for a user. This will help speed up the search for each neighbor. It is used in the content-boosted filter since it is a dense matrix and should not change based on which item a rating is being asked for. More...
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| multimap< double, ArrayWrapper > | getNeighbors (size_t user, size_t item) |
| | This is the same function as predict except that it returns the the priority queue for the nearest neigbors. The values are further user by the content-boosted cf prediction method to combine the content-based and cf predictions. More...
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| double | getRating (size_t user, size_t item) |
| | Get the rating of an item for a user. More...
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| virtual void | impute (GVec &vec, size_t dims) |
| | See the comment for GCollaborativeFilter::impute. More...
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| GSparseSimilarity * | metric () |
| | Returns the current similarity metric. (This might be useful, for example, if you want to modify the regularization value.) 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 | setMetric (GSparseSimilarity *pMetric, bool own) |
| | Sets the similarity metric to use. if own is true, then this object will take care of deleting it as appropriate. More...
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| void | setSigWeight (size_t sig) |
| | Set the value for significance weighting. The default value is zero. If there are fewer matching items between users than the specified values, the similarity is scaled by numMathces/sigWeight. 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