small doc tweaks

lenskit · Feb 10, 2022 · d2f4728 · d2f4728
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diff --git a/docs/knn.rst b/docs/knn.rst
@@ -14,7 +14,7 @@ mean-centering normalization.  This is the default mode, and can be selected exp
 With **implicit feedback** (unary data such as clicks and purchases, typically represented with
 rating values of 1 for positive items), the usual design is sum aggregation and no centering. This
 can be selected with ``feedback='implicit'``, which also configures the algorithm to ignore rating
-values (when present) and treat every rating as 1:
+values (when present) and treat every rating as 1::
 
     implicit_knn = ItemItem(20, feedback='implicit')
 

diff --git a/lenskit/algorithms/item_knn.py b/lenskit/algorithms/item_knn.py
@@ -222,8 +222,8 @@ class ItemItem(Predictor):
         nnbrs(int):
             the maximum number of neighbors for scoring each item (``None`` for unlimited)
         min_nbrs(int): the minimum number of neighbors for scoring each item
-        min_sim(double): minimum similarity threshold for considering a neighbor
-        save_nbrs(double):
+        min_sim(float): minimum similarity threshold for considering a neighbor
+        save_nbrs(float):
             the number of neighbors to save per item in the trained model
             (``None`` for unlimited)
         feedback(str):

diff --git a/lenskit/algorithms/user_knn.py b/lenskit/algorithms/user_knn.py
@@ -101,7 +101,7 @@ class UserUser(Predictor):
         nnbrs(int):
             the maximum number of neighbors for scoring each item (``None`` for unlimited)
         min_nbrs(int): the minimum number of neighbors for scoring each item
-        min_sim(double): minimum similarity threshold for considering a neighbor
+        min_sim(float): minimum similarity threshold for considering a neighbor
         feedback(str):
             Control how feedback should be interpreted.  Specifies defaults for the other
             settings, which can be overridden individually; can be one of the following values: