recommendation engine

A  recommendation engine  is software or a service that recommends things to people based on their behavior or people that have behaved like them in the past, like the features of sites that say things like people that bought this also bought that, or liked, or followed, etc. Such recommendations are also known as targeted resource recommendations, and are usually in a specific domain.

Why
Reasons to have a recommendation engine in your site / service / software include:


 * To improve search (rather than relying solely on keywords)
 * To suggest content after onboarding (rather than presenting the user with a blank or template page)
 * To help users follow each other (and therefore stimulate more conversation)

What to recommend
On your website, you could recommend:


 * Other articles for people to explore related to the content on a page.
 * Bookmarks to content you have read related to an article.
 * Podcasts or videos related to content on your site.

capjamesg
is building Novacast to add related internal links to his blog posts. This replaces a previous section that added related internal links using a sparse matrix similarity algorithm.
 * Example: https://jamesg.blog/2022/12/02/webmention-talk/



is building a WIP bookmarks recommendation engine on top of Novacast. Given a post title, the engine will return a list of related bookmarks. The engine works best when James has bookmarked a lot of content related to the subject.


 * Example: https://novacast.dev/u/jgbr?query=Adventures+on+the+IndieWeb

Amazon
Amazon recommends things to buy based on:
 * What you've bought in the past
 * What others have bought that you have bought
 * What others have bought after looking at the same page as you're on

Facebook
Facebook has a few features that appear to use a recommendation engine:
 * "People to follow" - their version of who to follow
 * Suggested Friends
 * Suggested Events
 * related events

Netflix
Netflix recommends movies to watch based on:
 * What you've watched and others have also watched

Twitter
Twitter has several features that appear to use a recommendation engine:
 * who to follow
 * suggested accounts when creating a new account (screenshot?)
 * suggested accounts to follow shown immediately after following someone (screenshot?)

Services to Silos

 * http://www.4-tell.com/

Tumblr
Tumblr recommends other blogs you should follow presumably based on blogs you already follow.

Also in the sidebar is a section called "radar" which shows and links to a single post.



When you click on Explore (the compass icon) you also get a list of topics and posts

When you click on a hashtag on Tumblr you get a list of posts plus related hashtags

In any of the recommendation tools you can sort by recency or by post types.



Brainstorming
A recommendation engine might recommend books based on titles you've already read, or blogs based on who you're following.

In an IndieWeb context, a recommendation engine could recommend


 * other independent website owners to follow an indie reader, based on
 * who you follow already
 * articles to read, based on
 * articles liked by people who have liked other articles that you've liked
 * liked/bookmarked/etc posts based on similarity towards the current entry; maybe with tag matching, text analysis. deep learning or manual sorting
 * YaCy might be used for indexing and for knitting together distinct communities that are scattered across many social networks

One of the benefits of an indieweb approach is that you might be able to supply your own algorithm, or have a choice of algorithm, rather than needing to trusting one provided by a silo.

The benefit might be a wider audience for independent site owners whose content might not otherwise be discovered.

With Microsub it would be possible to build a microsub "middleware" app that could keep track of what content is popular among its users and automatically create feeds of recommended content without needing to provide a full ui