Man pages for mhahsler/recommenderlab
Lab for Developing and Testing Recommender Algorithms
| binaryRatingMatrix-class | Class "binaryRatingMatrix": A Binary Rating Matrix |
| calcPredictionAccuracy | Calculate the Prediction Error for a Recommendation |
| dissimilarity | Dissimilarity and Similarity Calculation Between Rating Data |
| error | Error Calculation |
| evaluate | Evaluate a Recommender Models |
| evaluationResultList-class | Class "evaluationResultList": Results of the Evaluation of a... |
| evaluationResults-class | Class "evaluationResults": Results of the Evaluation of a... |
| evaluationScheme | Creator Function for evaluationScheme |
| evaluationScheme-class | Class "evaluationScheme": Evaluation Scheme |
| funkSVD | Funk SVD for Matrices with Missing Data |
| getList | List and Data.frame Representation for Recommender Matrix... |
| HybridRecommender | Create a Hybrid Recommender |
| internal | Internal Utility Functions |
| Jester5k | Jester dataset (5k sample) |
| MovieLense | MovieLense Dataset (100k) |
| MSWeb | Anonymous web data from www.microsoft.com |
| normalize | Normalize the ratings |
| plot | Plot Evaluation Results |
| predict | Predict Recommendations |
| ratingMatrix-class | Class "ratingMatrix": Virtual Class for Rating Data |
| realRatingMatrix-class | Class "realRatingMatrix": Real-valued Rating Matrix |
| Recommender | Create a Recommender Model |
| Recommender-class | Class "Recommender": A Recommender Model |
| sparseNAMatrix | Sparse Matrix Representation With NAs Not Explicitly Stored |
| topNList-class | Class "topNList": Top-N List |
