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> I started working on recommender systems in 2016 during my undergrad, specifically doing music recommendation (I was dissatisfied with the quality of Pandora/

> I started working on recommender systems in 2016 during my undergrad, specifically doing music recommendation (I was dissatisfied with the quality of Pandora/Spotify recommendations). I spent about five months in 2019 trying to make a music startup based on that. Hi! I'm very interested in this domain, I have long been dissatisfied with the results for music recommendation across almost every platform (and the respective apps, but I digress). So frequently it seems my own personal preferences are washed away in the breadth of ML and algorithmic recommendation systems, that no matter what I begin listening on, I will invariably find myself within the mainstream for that genre/artist. It also seems to me that many algorithms can't properly deduce that I would want songs centered around a specific year, and instead seem to draw from what's "popular" in the genre regardless of the time period the song is starting from. As an example, I can begin a GPM station based on I Gotta Feeling, a song from 2009. When I start a playlist like that, I nearly never am rewarded with a song from that time period. I tried it just now, it immediately jumped to Roar by Katy Perry, a song from 2014 with an entirely different vibe. I supposed my question is, how meaningful are the music recommendations Findka produces? I would love to talk more about the music startup you were pursuing, and what you accomplished there, as well as any roadblocks you may have faced. I have been whittling down an idea for a music startup myself and can't help but wonder where that road led for you. Also your site link is broken/not resolving.

tixocloud 6y ago
Pretty surprised that the music recommendation results you've been experiencing have been quite poor. Your feedback would have been invaluable in retraining the algorithm to make recommendations that suit your preference. Feels like a lost opportunity. Moreover, I don't know if much thought around self-discoverability/popularity has been factored into the design of the recommendation system - ideally, the recommendation system should surface interesting things that I like that I wouldn't necessarily discover on my own.
- jacobobryant 6y ago
  I've wondered about this a lot. It seems to be hit or miss--some people love e.g. Spotify's discover weekly/daily mix playlists, and some people hate them. I suspect that some users are outliers and it's hard to give them recommendations, and maybe tuning the system for them decreases the performance for other users. Also there may be limited business value in optimizing for those users anyway vs. pushing popular music.
  - tixocloud 6y ago
    I suspect you’re right about trading off between pushing popular music vs tuning for outlier users. Feels like there are too many smart engineers to miss it. Plus you’re building a personalisation system which should be personalising things for individual users by default.
jacobobryant 6y ago
I'd love to chat more about music etc. You can shoot me an email at hn at jacobobryant.com. Definitely a pain point I feel as well, though another reason I switched to Findka is because I figured it would be a lot easier to bootstrap it. The music industry is pretty tough to get started in. As far as the current music recommendations; it's nothing intelligent. Just pure collaborative filtering, i.e. it has no understanding of "this is a rock song" or "this song is from 2005" or even "this is a song". Right now, the algorithm just sees a bunch of URLs and their rating data. I am interested in making the algorithm more intelligent over time though. Could the site not resolving be an issue with your network? It Works For Me, and I'm getting plenty of traffic right now.

tixocloud 6y ago

Pretty surprised that the music recommendation results you've been experiencing have been quite poor. Your feedback would have been invaluable in retraining the algorithm to make recommendations that suit your preference. Feels like a lost opportunity. Moreover, I don't know if much thought around self-discoverability/popularity has been factored into the design of the recommendation system - ideally, the recommendation system should surface interesting things that I like that I wouldn't necessarily discover on my own.

jacobobryant 6y ago
I've wondered about this a lot. It seems to be hit or miss--some people love e.g. Spotify's discover weekly/daily mix playlists, and some people hate them. I suspect that some users are outliers and it's hard to give them recommendations, and maybe tuning the system for them decreases the performance for other users. Also there may be limited business value in optimizing for those users anyway vs. pushing popular music.
- tixocloud 6y ago
  I suspect you’re right about trading off between pushing popular music vs tuning for outlier users. Feels like there are too many smart engineers to miss it. Plus you’re building a personalisation system which should be personalising things for individual users by default.

jacobobryant 6y ago

I'd love to chat more about music etc. You can shoot me an email at hn at jacobobryant.com. Definitely a pain point I feel as well, though another reason I switched to Findka is because I figured it would be a lot easier to bootstrap it. The music industry is pretty tough to get started in. As far as the current music recommendations; it's nothing intelligent. Just pure collaborative filtering, i.e. it has no understanding of "this is a rock song" or "this song is from 2005" or even "this is a song". Right now, the algorithm just sees a bunch of URLs and their rating data. I am interested in making the algorithm more intelligent over time though. Could the site not resolving be an issue with your network? It Works For Me, and I'm getting plenty of traffic right now.