diff --git a/DESCRIPTION b/DESCRIPTION
index ca0b632..261cba3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -42,5 +42,5 @@ Description: Collective matrix factorization (a.k.a. multi-view or multi-way fac
 License: MIT + file LICENSE
 Suggests: Matrix, MatrixExtra, RhpcBLASctl, recosystem (>= 0.5), recommenderlab (>= 0.2-7), MASS, knitr, rmarkdown, kableExtra
 VignetteBuilder: knitr
-RoxygenNote: 7.2.2
+RoxygenNote: 7.2.3
 NeedsCompilation: yes
diff --git a/R/fit.R b/R/fit.R
index 620f0d7..497cf10 100644
--- a/R/fit.R
+++ b/R/fit.R
@@ -48,7 +48,8 @@ NULL
 #' and the objective is to minimize squared error over the non-missing entries, in the
 #' implicit-feedback variants the matrix `X` is assumed to be binary (all entries are zero
 #' or one, with no unknown values), with the positive entries (those which are not
-#' missing in the data) having a weight determined by `X`.
+#' missing in the data) having a weight determined by `X`, and without including any
+#' user/item biases or centering for the 'X' matrix.
 #' 
 #' `CMF` is intended for explicit feedback data (e.g. movie ratings, which contain both
 #' likes and dislikes), whereas `CMF_implicit` is intended for implicit feedback data
diff --git a/cmfrec/__init__.py b/cmfrec/__init__.py
index 9eac264..311d457 100644
--- a/cmfrec/__init__.py
+++ b/cmfrec/__init__.py
@@ -4412,6 +4412,14 @@ class CMF_implicit(_CMF):
 
         :math:`\mathbf{I} \sim \mathbf{B} \mathbf{D}^T`
 
+    Compared to the ``CMF`` class, here the interactions matrix 'X' treats missing
+    entries as zeros and non-missing entries as ones, while the values supplied for
+    interactions are applied as weights over this binarized matrix 'X' (see references
+    for more details). Roughly speaking, it is a more efficient version of `CMF` with
+    hard-coded arguments ``NA_as_zero=True``, ``center=False``, ``user_bias=False``,
+    ``item_bias=False``, ``scale_lam=False``, plus a different initialization of factor
+    matrices, and 'X' converted to a weighted binary matrix as explained earlier.
+
     Note
     ----
     The default hyperparameters in this software are very different from others.
diff --git a/man/fit.Rd b/man/fit.Rd
index c4cf01a..7bf6a51 100644
--- a/man/fit.Rd
+++ b/man/fit.Rd
@@ -780,7 +780,8 @@ case, based on reference [3]. While in `CMF` the values of `X` are taken at face
 and the objective is to minimize squared error over the non-missing entries, in the
 implicit-feedback variants the matrix `X` is assumed to be binary (all entries are zero
 or one, with no unknown values), with the positive entries (those which are not
-missing in the data) having a weight determined by `X`.
+missing in the data) having a weight determined by `X`, and without including any
+user/item biases or centering for the 'X' matrix.
 
 `CMF` is intended for explicit feedback data (e.g. movie ratings, which contain both
 likes and dislikes), whereas `CMF_implicit` is intended for implicit feedback data