cf: Collaborative Filtering

View source: R/cf.R

cfR Documentation

Collaborative Filtering


An implementation of several collaborative filtering (CF) techniques for recommender systems. This can be used to train a new CF model, or use an existing CF model to compute recommendations.


  algorithm = NA,
  all_user_recommendations = FALSE,
  input_model = NA,
  interpolation = NA,
  iteration_only_termination = FALSE,
  max_iterations = NA,
  min_residue = NA,
  neighbor_search = NA,
  neighborhood = NA,
  normalization = NA,
  query = NA,
  rank = NA,
  recommendations = NA,
  seed = NA,
  test = NA,
  training = NA,
  verbose = FALSE



Algorithm used for matrix factorization. Default value "NMF" (character).


Generate recommendations for all users. Default value "FALSE" (logical).


Trained CF model to load (CFModel).


Algorithm used for weight interpolation. Default value "average" (character).


Terminate only when the maximum number of iterations is reached. Default value "FALSE" (logical).


Maximum number of iterations. If set to zero, there is no limit on the number of iterations. Default value "1000" (integer).


Residue required to terminate the factorization (lower values generally mean better fits). Default value "1e-05" (numeric).


Algorithm used for neighbor search. Default value "euclidean" (character).


Size of the neighborhood of similar users to consider for each query user. Default value "5" (integer).


Normalization performed on the ratings. Default value "none" (character).


List of query users for which recommendations should be generated (integer matrix).


Rank of decomposed matrices (if 0, a heuristic is used to estimate the rank). Default value "0" (integer).


Number of recommendations to generate for each query user. Default value "5" (integer).


Set the random seed (0 uses std::time(NULL)). Default value "0" (integer).


Test set to calculate RMSE on (numeric matrix).


Input dataset to perform CF on (numeric matrix).


Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical).


This program performs collaborative filtering (CF) on the given dataset. Given a list of user, item and preferences (the "training" parameter), the program will perform a matrix decomposition and then can perform a series of actions related to collaborative filtering. Alternately, the program can load an existing saved CF model with the "input_model" parameter and then use that model to provide recommendations or predict values.

The input matrix should be a 3-dimensional matrix of ratings, where the first dimension is the user, the second dimension is the item, and the third dimension is that user's rating of that item. Both the users and items should be numeric indices, not names. The indices are assumed to start from 0.

A set of query users for which recommendations can be generated may be specified with the "query" parameter; alternately, recommendations may be generated for every user in the dataset by specifying the "all_user_recommendations" parameter. In addition, the number of recommendations per user to generate can be specified with the "recommendations" parameter, and the number of similar users (the size of the neighborhood) to be considered when generating recommendations can be specified with the "neighborhood" parameter.

For performing the matrix decomposition, the following optimization algorithms can be specified via the "algorithm" parameter: - 'RegSVD' – Regularized SVD using a SGD optimizer - 'NMF' – Non-negative matrix factorization with alternating least squares update rules - 'BatchSVD' – SVD batch learning - 'SVDIncompleteIncremental' – SVD incomplete incremental learning - 'SVDCompleteIncremental' – SVD complete incremental learning - 'BiasSVD' – Bias SVD using a SGD optimizer - 'SVDPP' – SVD++ using a SGD optimizer

The following neighbor search algorithms can be specified via the "neighbor_search" parameter: - 'cosine' – Cosine Search Algorithm - 'euclidean' – Euclidean Search Algorithm - 'pearson' – Pearson Search Algorithm

The following weight interpolation algorithms can be specified via the "interpolation" parameter: - 'average' – Average Interpolation Algorithm - 'regression' – Regression Interpolation Algorithm - 'similarity' – Similarity Interpolation Algorithm

The following ranking normalization algorithms can be specified via the "normalization" parameter: - 'none' – No Normalization - 'item_mean' – Item Mean Normalization - 'overall_mean' – Overall Mean Normalization - 'user_mean' – User Mean Normalization - 'z_score' – Z-Score Normalization

A trained model may be saved to with the "output_model" output parameter.


A list with several components:


Matrix that will store output recommendations (integer matrix).


Output for trained CF model (CFModel).


mlpack developers


# To train a CF model on a dataset "training_set" using NMF for decomposition
# and saving the trained model to "model", one could call: 

## Not run: 
output <- cf(training=training_set, algorithm="NMF")
model <- output$output_model

## End(Not run)

# Then, to use this model to generate recommendations for the list of users
# in the query set "users", storing 5 recommendations in "recommendations",
# one could call 

## Not run: 
output <- cf(input_model=model, query=users, recommendations=5)
recommendations <- output$output

## End(Not run)

mlpack documentation built on Oct. 29, 2022, 1:06 a.m.

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