Lab for Developing and Testing Recommender Algorithms
Class "binaryRatingMatrix": A Binary Rating Matrix
Calculate the Prediction Error for a Recommendation
Dissimilarity and Similarity Calculation Between Rating Data
Error Calculation
Evaluate a Recommender Models
Class "evaluationResultList": Results of the Evaluation of a Multiple ...
Class "evaluationResults": Results of the Evaluation of a Single Recom...
Class "evaluationScheme": Evaluation Scheme
Creator Function for evaluationScheme
Funk SVD for Matrices with Missing Data
List and Data.frame Representation for Recommender Matrix Objects
Create a Hybrid Recommender
Internal Utility Functions
Normalize the ratings
Plot Evaluation Results
Predict Recommendations
Class "ratingMatrix": Virtual Class for Rating Data
Class "realRatingMatrix": Real-valued Rating Matrix
Class "Recommender": A Recommender Model
Create a Recommender Model
Sparse Matrix Representation With NAs Not Explicitly Stored
Class "topNList": Top-N List
Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.
Useful links