README.md in disco-0.1.1 vs README.md in disco-0.1.2

@@ -2,11 +2,11 @@
 
 :fire: Collaborative filtering for Ruby
 
 - Supports user-based and item-based recommendations
 - Works with explicit and implicit feedback
-- Uses matrix factorization
+- Uses high-performance matrix factorization
 
 [![Build Status](https://travis-ci.org/ankane/disco.svg?branch=master)](https://travis-ci.org/ankane/disco)
 
 ## Installation
 
@@ -200,11 +200,11 @@
 recommender = Marshal.load(bin)
 ```
 
 ## Algorithms
 
-Disco uses matrix factorization.
+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)
 - For implicit feedback, it uses [coordinate descent](https://www.csie.ntu.edu.tw/~cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf)
 
 Specify the number of factors and epochs
@@ -234,17 +234,46 @@
 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).
 
-## Daru
+## Data
 
-Disco works with Daru data frames
+Data can be an array of hashes
 
 ```ruby
-data = Daru::DataFrame.from_csv("ratings.csv")
-recommender.fit(data)
+[{user_id: 1, item_id: 1, rating: 5}, {user_id: 2, item_id: 1, rating: 3}]
 ```
+
+Or a Daru data frame
+
+```ruby
+Daru::DataFrame.from_csv("ratings.csv")
+```
+
+## Faster Similarity [experimental]
+
+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.
+
+Add this line to your application’s Gemfile:
+
+```ruby
+gem 'ngt', '>= 0.2.3'
+```
+
+Speed up item-based recommendations with:
+
+```ruby
+model.optimize_item_recs
+```
+
+Speed up similar users with:
+
+```ruby
+model.optimize_similar_users
+```
+
+This should be called after fitting or loading the model.
 
 ## Reference
 
 Get the global mean