expand docs about CMF_implicit ref #40

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

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

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.