README.md in disco-0.2.4 vs README.md in disco-0.2.5

@@ -199,10 +199,12 @@
 ```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)
+
 ## Algorithms
 
 Disco uses high-performance matrix factorization.
 
 - For explicit feedback, it uses [stochastic gradient descent](https://www.csie.ntu.edu.tw/~cjlin/papers/libmf/libmf_journal.pdf)
@@ -235,10 +237,20 @@
 There are a number of ways to deal with this, but here are some common ones:
 
 - For user-based recommendations, show new users the most popular items.
 - For item-based recommendations, make content-based recommendations with a gem like [tf-idf-similarity](https://github.com/jpmckinney/tf-idf-similarity).
 
+Get top items with:
+
+```ruby
+recommender = Disco::Recommender.new(top_items: true)
+recommender.fit(data)
+recommender.top_items
+```
+
+This uses [Wilson score](https://www.evanmiller.org/how-not-to-sort-by-average-rating.html) for explicit feedback (add [wilson_score](https://github.com/instacart/wilson_score) your application’s Gemfile) and item frequency for implicit feedback.
+
 ## Data
 
 Data can be an array of hashes
 
 ```ruby
@@ -255,26 +267,32 @@
 
 ```ruby
 Daru::DataFrame.from_csv("ratings.csv")
 ```
 
-## Faster Similarity
+## Performance [master]
 
-If you have a large number of users/items, you can use an approximate nearest neighbors library like [NGT](https://github.com/ankane/ngt) to speed up item-based recommendations and similar users.
+If you have a large number of users or items, you can use an approximate nearest neighbors library like [Faiss](https://github.com/ankane/faiss) to improve the performance of certain methods.
 
 Add this line to your application’s Gemfile:
 
 ```ruby
-gem 'ngt', '>= 0.3.0'
+gem 'faiss'
 ```
 
-Speed up item-based recommendations with:
+Speed up the `user_recs` method with:
 
 ```ruby
+model.optimize_user_recs
+```
+
+Speed up the `item_recs` method with:
+
+```ruby
 model.optimize_item_recs
 ```
 
-Speed up similar users with:
+Speed up the `similar_users` method with:
 
 ```ruby
 model.optimize_similar_users
 ```