Minor documentation fixes (#609)

docs/source/index.rst

@@ -33,7 +33,7 @@ Basic Usage
     # initialize a model
     model = implicit.als.AlternatingLeastSquares(factors=64)
 
-    # train the model on a sparse matrix of item/user/confidence weights
+    # train the model on a sparse matrix of user/item/confidence weights
     model.fit(user_item_data)
 
     # recommend items for a user

examples/tutorial_lastfm.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "`artist_user_plays` is a scipy sparse matrix, with the each row corresponding to a different musician and each column corresponding to a different user. The non-zero entries in the `artist_user_plays` matrix contains the number of times that the user has played that artist. The `artists` and `users` variables are arrays of string labels for each row and column in the `artist_user_plays` matrix. \n",
     "\n",
-    "The implicit library is solely focused on implicit feedback recommenders systems - where we are given positive examples of what the user has interacted with, but aren't given the corresponding negative examples of what users aren't interested in. For this example we're shown the number of times that the user has played an artist in the dataset and can infer that a high play count indicates that the user likes an artist. However we can't infer that just because the user hasn't played an band before that means the user doesn't like the band."
+    "The implicit library is solely focused on implicit feedback recommenders systems - where we are given positive examples of what the user has interacted with, but aren't given the corresponding negative examples of what users aren't interested in. For this example we're shown the number of times that the user has played an artist in the dataset and can infer that a high play count indicates that the user likes an artist. However we can't infer that just because the user hasn't played a band before that means the user doesn't like the band."
    ]
   },
   {

implicit/gpu/matrix.h

@@ -27,7 +27,7 @@ struct Matrix {
   // underlying storage buffer is shared in this case.
   Matrix(const Matrix &other, size_t rowid);
 
-  // Slice a contiguous series of rows from this Matrix. The underlying storge
+  // Slice a contiguous series of rows from this Matrix. The underlying storage
   // buffer is shared here.
   Matrix(const Matrix &other, size_t start_rowid, size_t end_rowid);
 

implicit/recommender_base.py

@@ -15,7 +15,7 @@ class RecommenderBase(metaclass=ABCMeta):
     @abstractmethod
     def fit(self, user_items, show_progress=True, fit_callback=None):
         """
-        Trains the model on a sparse matrix of item/user/weight
+        Trains the model on a sparse matrix of user/item/weight
 
         Parameters
         ----------