README.md in disco-0.4.0 vs README.md in disco-0.4.2
- old
+ new
@@ -4,11 +4,11 @@
- Supports user-based and item-based recommendations
- Works with explicit and implicit feedback
- Uses high-performance matrix factorization
-[](https://github.com/ankane/disco/actions)
+[](https://github.com/ankane/disco/actions)
## Installation
Add this line to your application’s Gemfile:
@@ -227,12 +227,12 @@
recommender.user_recs(new_user_id) # returns empty array
```
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).
+- 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)
@@ -326,31 +326,9 @@
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)