cmfrec

Collective Matrix Factorization for Recommender Systems

CRAN Package

Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969> tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <doi:10.48550/arXiv.1809.00366>) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) <doi:10.48550/arXiv.1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.


Documentation


Team


Insights

Last 30 days

This package has been downloaded 671 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 times.

Sun
Mon
Tue
Wed
Thu
Fri
Sat
0 downloadsFeb 9, 2025
0 downloadsFeb 10, 2025
0 downloadsFeb 11, 2025
69 downloadsFeb 12, 2025
32 downloadsFeb 13, 2025
7 downloadsFeb 14, 2025
27 downloadsFeb 15, 2025
5 downloadsFeb 16, 2025
28 downloadsFeb 17, 2025
6 downloadsFeb 18, 2025
39 downloadsFeb 19, 2025
59 downloadsFeb 20, 2025
35 downloadsFeb 21, 2025
11 downloadsFeb 22, 2025
32 downloadsFeb 23, 2025
20 downloadsFeb 24, 2025
11 downloadsFeb 25, 2025
41 downloadsFeb 26, 2025
26 downloadsFeb 27, 2025
6 downloadsFeb 28, 2025
7 downloadsMar 1, 2025
12 downloadsMar 2, 2025
6 downloadsMar 3, 2025
19 downloadsMar 4, 2025
71 downloadsMar 5, 2025
7 downloadsMar 6, 2025
11 downloadsMar 7, 2025
9 downloadsMar 8, 2025
9 downloadsMar 9, 2025
7 downloadsMar 10, 2025
35 downloadsMar 11, 2025
16 downloadsMar 12, 2025
8 downloadsMar 13, 2025
0 downloadsMar 14, 2025
0 downloadsMar 15, 2025
5
71

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,938 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Sep 26, 2024 with 80 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Data provided by CRAN


Binaries


Dependencies

  • Suggests9 packages
  • Reverse Suggests1 package