Hi all! I'm Alexi, founder of Tamber. We built Tamber to help developers put fast, effective recommendations into their apps.<p>After trying a few open source libraries for a music app I was building, I found that they were surprisingly tedious to implement and tended to overfit for popular items – Neil Young <i>is</i> similar to Bob Dylan, but that doesn't help you discover new music. I knew there had to be a better approach that would solve this popularity bias problem, and make recommendations less painful to implement.<p>Tamber overcomes popularity bias by learning not only the relationships between items, but also how trends in taste evolve over time and using that information to boost less-well-known items in recommendations.<p>It works just like an analytics service, except that every event you track triggers a system-wide update to the model. And it's really fast, returning fresh suggestions in 20-120ms. So as a user navigates around your app (even if they aren't signed in!) your app can always display the optimal set of next things they should see next.<p>Here is a simple demo app for book recommendations we made using Goodreads data pulled from Kaggle: <a href="https://tamber.com/demo/goodbooks" rel="nofollow">https://tamber.com/demo/goodbooks</a><p>I'll open source the app code once I clean it up a bit.<p>Looking forward to hearing your thoughts and feedback!
Our company has been using this for a few months now and it’s seriously awesome - has saved our developers about 100 hours of work...paid for itself 10X over and seeing a bump in user time in product