README.md in disco-0.2.6 vs README.md in disco-0.2.7

@@ -33,21 +33,19 @@
 ])
 ```
 
 > IDs can be integers, strings, or any other data type
 
-If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating, or use a value like number of purchases, number of page views, or time spent on page:
+If users don’t rate items directly (for instance, they’re purchasing items or reading posts), this is known as implicit feedback. Leave out the rating.
 
 ```ruby
 recommender.fit([
-  {user_id: 1, item_id: 1, value: 1},
-  {user_id: 2, item_id: 1, value: 1}
+  {user_id: 1, item_id: 1},
+  {user_id: 2, item_id: 1}
 ])
 ```
 
-> Use `value` instead of `rating` for implicit feedback
-
 Get user-based recommendations - “users like you also liked”
 
 ```ruby
 recommender.user_recs(user_id)
 ```
@@ -104,15 +102,14 @@
   group(:user_id).
   group("properties->>'post_id'"). # postgres syntax
   count
 
 data =
-  views.map do |(user_id, post_id), count|
+  views.map do |(user_id, post_id), _|
     {
       user_id: user_id,
-      item_id: post_id,
-      value: count
+      item_id: post_id
     }
   end
 ```
 
 Create a recommender and get recommended posts for a user
@@ -199,11 +196,11 @@
 ```ruby
 bin = File.binread("recommender.bin")
 recommender = Marshal.load(bin)
 ```
 
-Alternatively, you can store only the factors and use a library like [Neighbor](https://github.com/ankane/neighbor)
+Alternatively, you can store only the factors and use a library like [Neighbor](https://github.com/ankane/neighbor). See the [examples](https://github.com/ankane/neighbor/tree/master/examples).
 
 ## Algorithms
 
 Disco uses high-performance matrix factorization.
 
@@ -280,26 +277,26 @@
 ```
 
 Speed up the `user_recs` method with:
 
 ```ruby
-model.optimize_user_recs
+recommender.optimize_user_recs
 ```
 
 Speed up the `item_recs` method with:
 
 ```ruby
-model.optimize_item_recs
+recommender.optimize_item_recs
 ```
 
 Speed up the `similar_users` method with:
 
 ```ruby
-model.optimize_similar_users
+recommender.optimize_similar_users
 ```
 
-This should be called after fitting or loading the model.
+This should be called after fitting or loading the recommender.
 
 ## Reference
 
 Get ids
 
@@ -333,9 +330,31 @@
 Thanks to:
 
 - [LIBMF](https://github.com/cjlin1/libmf) for providing high performance matrix factorization
 - [Implicit](https://github.com/benfred/implicit/) for serving as an initial reference for user and item similarity
 - [@dasch](https://github.com/dasch) for the gem name
+
+## Upgrading
+
+### 0.2.7
+
+There’s now a warning when passing `:value` with implicit feedback, as this has no effect on recommendations and can be removed. Earlier versions of the library incorrectly stated this was used.
+
+```ruby
+recommender.fit([
+  {user_id: 1, item_id: 1, value: 1},
+  {user_id: 2, item_id: 1, value: 3}
+])
+```
+
+to:
+
+```ruby
+recommender.fit([
+  {user_id: 1, item_id: 1},
+  {user_id: 2, item_id: 1}
+])
+```
 
 ## History
 
 View the [changelog](https://github.com/ankane/disco/blob/master/CHANGELOG.md)