cf: Collaborative Filtering

In mlpack: 'Rcpp' Integration for the 'mlpack' Library

Collaborative Filtering

Description

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.

Usage

cf(
  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 = getOption("mlpack.verbose", FALSE)
)

Arguments

algorithm	Algorithm used for matrix factorization. Default value "NMF" (character).
all_user_recommendations	Generate recommendations for all users. Default value "FALSE" (logical).
input_model	Trained CF model to load (CFModel).
interpolation	Algorithm used for weight interpolation. Default value "average" (character).
iteration_only_termination	Terminate only when the maximum number of iterations is reached. Default value "FALSE" (logical).
max_iterations	Maximum number of iterations. If set to zero, there is no limit on the number of iterations. Default value "1000" (integer).
min_residue	Residue required to terminate the factorization (lower values generally mean better fits). Default value "1e-05" (numeric).
neighbor_search	Algorithm used for neighbor search. Default value "euclidean" (character).
neighborhood	Size of the neighborhood of similar users to consider for each query user. Default value "5" (integer).
normalization	Normalization performed on the ratings. Default value "none" (character).
query	List of query users for which recommendations should be generated (integer matrix).
rank	Rank of decomposed matrices (if 0, a heuristic is used to estimate the rank). Default value "0" (integer).
recommendations	Number of recommendations to generate for each query user. Default value "5" (integer).
seed	Set the random seed (0 uses std::time(NULL)). Default value "0" (integer).
test	Test set to calculate RMSE on (numeric matrix).
training	Input dataset to perform CF on (numeric matrix).
verbose	Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Details

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 - 'RandSVD' – RandomizedSVD learning - 'QSVD' – QuicSVD learning - 'BKSVD' – Block Krylov SVD learning

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.

Value

A list with several components:

output	Matrix that will store output recommendations (integer matrix).
output_model	Output for trained CF model (CFModel).

Author(s)

mlpack developers

Examples

# 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. 5, 2024, 9:08 a.m.