It would be great if everybody gave their opinion about the recommendation systems they've tried. I'll start:

Netflix < MovieLens < Criticker < Coollector Movie Database

These days I never know if an angle bracket is being used as a priority indicator or inequality operator.

This app already exists for Netflix (25 countries) and Hulu:

https://www.coollector.com/#netflix

It's clearly what happened to Netflix. Do you remember when Netflix was the infamous leader in movie recommendations? Do you remember the Netflix prize?

Netflix recommendation system is a joke today, but it used to be much better in the past. Executives have decided to dumb it down for marketing reasons (promoting their own shows, hiding how their catalog is limited, etc...).

> No one has come close to solving recommendation.

Try this app and we'll talk:

https://www.coollector.com/help.html#recommendations

> That is part of the reason the recommender has been degrading over the years.

Netflix is clearly promoting its original shows. Do you think your system is still in use now that they've moved to thumbs rating and percent match score?

That is what I have been told. It is however clearly messed up.

I feel for you. It must be a huge disappointment to see what they've done to your work. Netflix recommendation system used to be very good a few years ago.

I've built a recommender system for my movie database app "Coollector Movie Database". It's based on Collaborative filtering and it took me 2 years to implement. I built it from scratch and it's unique in several ways (for example, you can view the reliability of each recommendation). The technical difficulty is to crunch fast enough a huge quantity of data. I've had to apply all the optimizations that I could think of.

https://www.coollector.com/help.html#recommendations

That is a pretty amazing site! Can't download anything right now which makes me think It would make it a lot easier if instead of a downloadable software it was being offered as a saas site.

Thank you! It could not be free if it was a website because calculating the recommendations uses quite a lot of CPU. It's fine when run on each user's computer, but it would require expensive servers to make all the calculations for everyone, with a risk of congestion.

There's a website named criticker.com which gives great movie recommendations, but you'll see that they have problems handling all the calculations. You'll literally see the recommendations being slowly generated, and updates are a problem when you rate more movies.

How would you generalize the method you are using?

I don't like ML frameworks (Tensorflow, etc...), maybe it's because I haven't tried them. My understanding is that they're like a magic black box: you input some data, you adjust some settings, and you wish for the results to be good. Instead, I've taken a direct approach to the collaborative filtering problem, the difficulty being to correlate a huge amount of data. Some said that only quantum computers would one day be fast enough to solve the recommendation problem, until recently a student demonstrated that it could be solved with classical computers.

https://www.quantamagazine.org/teenager-finds-classical-alte...

This student's algorithm is quite different from mine, but I suppose that my algorithm is yet another example of solving the recommendation problem with classical computers.

As long as the users rate how much they like something, my method could easily work with songs or books or anything.

Flixbox is USA only, but there are some websites like unogs.com who can handle more countries. In addition to a more convenient browsing of the catalog, if you also want an alternative recommendation system (with really accurate and unbiased predicted ratings), there's the Windows/Mac app Coollector Movie Database: https://www.coollector.com/

Thankfully there are alternative interfaces to browse the Netflix catalog:

https://www.coollector.com/index.html#netflix