README.md in disco-0.3.1 vs README.md in disco-0.3.2

@@ -185,14 +185,12 @@
 ```ruby
 json = recommender.to_json
 File.write("recommender.json", json)
 ```
 
-> You can save it to a file, database, or any other storage system. Also, user and item IDs should be integers or strings for this.
+The serialized recommender includes user activity from the training data (to avoid recommending previously rated items), so be sure to protect it. You can save it to a file, database, or any other storage system, or use a tool like [Trove](https://github.com/ankane/trove). Also, user and item IDs should be integers or strings for this.
 
-The serialized recommender includes user activity from the training data (to avoid recommending previously rated items), so be sure to protect it.
-
 Load a recommender
 
 ```ruby
 json = File.read("recommender.json")
 recommender = Disco::Recommender.load_json(json)
@@ -223,10 +221,10 @@
 recommender.fit(data, validation_set: validation_set)
 ```
 
 ## Cold Start
 
-Collaborative filtering suffers from the [cold start problem](https://www.yuspify.com/blog/cold-start-problem-recommender-systems/). It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.
+Collaborative filtering suffers from the [cold start problem](https://en.wikipedia.org/wiki/Cold_start_(recommender_systems)). It’s unable to make good recommendations without data on a user or item, which is problematic for new users and items.
 
 ```ruby
 recommender.user_recs(new_user_id) # returns empty array
 ```