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This would be much better if it allowed you to import ratings from other popular rating services (letterboxd, goodreads, RYM, etc.)

jacobobryant 6y ago
I did this in a previous version of Findka. I had integrations for Last.fm, Spotify, Youtube, Goodreads, Pocket, RSS and a couple others I think. I did a "reset" on Findka in February, removing most of the features in order to make it simpler. Eventually I would like to add integrations back in. I'm working on some social networking features right now--hopefully that'll work as a reliable user acquisition channel. Once I've figured out a way to keep getting new users, I'll be able to spend more time on things like integrations and improving the algorithm. Also I hadn't heard of letterboxd or RYM. I'll check those out more when the time comes.
- nautist 6y ago
  > Eventually I would like to add integrations back in I might be missing the big picture, but wouldn't log-in with oauth[0] solve integration problem and removed impediment from new users coming in? Also, if you do not mind, which algo do you use to find matches? [0] https://en.m.wikipedia.org/wiki/List_of_OAuth_providers https://en.m.wikipedia.org/wiki/List_of_OAuth_providers Edit: formatting
  - jacobobryant 6y ago
    I don't think it'd make any difference. In the list you linked, only a few of those provide log-in with oauth. Letting people log in with Google won't let us import data from e.g. Letterboxd etc. Even if Letterboxd provided log-in with oauth, you'd still have to go through another authorization flow if you wanted to import data from an additional service. The algorithm uses a simple item-based neighborhood model. i.e. if you like song A, Findka looks up all the content liked by other users who liked song A and probabilistically chooses an item that was well-liked. To help the algorithm keep learning, 35% of the recommendations are purely random ("epsilon-greedy"). I describe the implementation here[0], though it's changed slightly (now I export the database every day or so and generate a model on my laptop, then I load it into memory on the server). I experimented with a machine learning-based model (a latent factor model) last week, but it seems I don't yet have enough rating data for that to be useful. [0] https://findka.com/blog/rec-sys-in-30-lines/ https://findka.com/blog/rec-sys-in-30-lines/

jacobobryant 6y ago

I did this in a previous version of Findka. I had integrations for Last.fm, Spotify, Youtube, Goodreads, Pocket, RSS and a couple others I think. I did a "reset" on Findka in February, removing most of the features in order to make it simpler. Eventually I would like to add integrations back in. I'm working on some social networking features right now--hopefully that'll work as a reliable user acquisition channel. Once I've figured out a way to keep getting new users, I'll be able to spend more time on things like integrations and improving the algorithm. Also I hadn't heard of letterboxd or RYM. I'll check those out more when the time comes.

nautist 6y ago
> Eventually I would like to add integrations back in I might be missing the big picture, but wouldn't log-in with oauth[0] solve integration problem and removed impediment from new users coming in? Also, if you do not mind, which algo do you use to find matches? [0] https://en.m.wikipedia.org/wiki/List_of_OAuth_providers https://en.m.wikipedia.org/wiki/List_of_OAuth_providers Edit: formatting
- jacobobryant 6y ago
  I don't think it'd make any difference. In the list you linked, only a few of those provide log-in with oauth. Letting people log in with Google won't let us import data from e.g. Letterboxd etc. Even if Letterboxd provided log-in with oauth, you'd still have to go through another authorization flow if you wanted to import data from an additional service. The algorithm uses a simple item-based neighborhood model. i.e. if you like song A, Findka looks up all the content liked by other users who liked song A and probabilistically chooses an item that was well-liked. To help the algorithm keep learning, 35% of the recommendations are purely random ("epsilon-greedy"). I describe the implementation here[0], though it's changed slightly (now I export the database every day or so and generate a model on my laptop, then I load it into memory on the server). I experimented with a machine learning-based model (a latent factor model) last week, but it seems I don't yet have enough rating data for that to be useful. [0] https://findka.com/blog/rec-sys-in-30-lines/ https://findka.com/blog/rec-sys-in-30-lines/