matrix-doc VS Surprise

Group voice and video calls are not E2EE, and use Jitsi, but this is expected to change with Native Group VoIP Signalling.

DMs in Matrix are always E2EE, and MSC3401: Native Group VoIP Signalling means there should be E2EE in group calls.

From my perspective, the really exciting thing about this that it works equally well in mobile web browsers as well as desktop web - clicking on a link on Mobile Safari should Do The Right Thing without having to install anything.

Moreover, because it's built on Matrix, MSC3401 (https://github.com/matrix-org/matrix-doc/blob/matthew/group-...) means that we'll finally have decentralised cascading video/voice conferences once the SFU (selective forwarding unit) component is added into the mix. So, for instance, users on the same homeserver will get their video feeds relayed locally with minimal latency... and then users on another remote homeserver will also get mixed locally with minimal latency, trunking the two together. If the link dies or one homeserver dies, the conference will keep going - i.e. precisely the same semantics as normal Matrix.

What metadata does Matrix protect? Encrypted state events still aren't a thing for example https://github.com/matrix-org/matrix-doc/pull/3414 This means that server admins know what groups a given account is a member of, private or not, and they also have a general idea of what the topic of said groups are, even if they're encrypted. This would be a problem for groups about sensitive personal medical issues, like a private HIV survivors or Alcoholics Anonymous group.

The protocol itself is flexible and can be changed through spec change proposals on their Github. They're currently working on implementing threads, and they recently implemented spaces, which functionally combine the concept of Discord servers and server folders. They can also be nested.

At the moment this only works with Jitsi. It will be implementet soon with MSC3401

Something we're trying to do about this on the Matrix side is MSC2716 (https://github.com/matrix-org/matrix-doc/blob/matthew/msc271...) - the ability to import archives of existing content into Matrix, and thus 'lock it open' and decentralise it for posterity: as long as one of the servers participating in that room stays alive (and the room is set up with infinite data retention, obviously) then the conversation will live on forever. (That MSC is also well worth a look for those interested in how Matrix works under the hood; MSC2716 was a surprisingly tricky problem to solve but it's basically finished now!).

Our first step will be to import all of Gitter's archives into Matrix - but we're then planning to add MSC2716 to all the existing Matrix bridges so that folks can use it to liberate chat history from Discord and Slack if desired, and avoid it getting paywalled/siloed/lost/held-hostage forever. We're also expecting to do USENET, mailing lists, forums, public IRC channels which have explicitly opted into logging... and generally archive as much possible in an open decentralised fashion, and ensure that gatekeepers can't lock up and blackhole info going forwards. After all, information longs to be free :)

by 'broken links' i guess you mean https://github.com/matrix-org/matrix-doc/issues/3628? it's a bug on the new spec website; we're working on it.

Something is wrong with the repo. The compiler fails with this error clang: error: no such file or directory: 'surprise/similarities.c' If you go to the repo, you'll see the file is indeed missing: https://github.com/NicolasHug/Surprise/tree/master/surprise

Scikit-surprise is a useful package and has pretty good documentation to help make the leap from conceptual understanding to code. If you want to understand the various implementations, the package is open source and available on GitHub. I can’t speak for optimal computational efficiency but I think that it’s premature to worry about that while you’re still making the transition from concept to functionality.

I spent the latter half of 2019 trying to build this as a startup. Ultimately I pivoted (now I do newsletter recommendations instead), but if I hadn't made some mistakes I think it could've gotten more traction. Mostly I should've simplified the idea to make it easier to build. If anyone's interested in working on this, here's what I would do:

(But first some background: The way I saw it, you can split music recommendation into two tasks: (1) picking a song you already know that should be played right now, and (2) picking a new song you've never heard of before. (Music recommendation is unique in this way since in most other domains there isn't much value in re-recommending items). I think #1 is more important, and if you nail that, you can do a so-so job of #2 and still have a good system.)

Make a website that imports your Last.fm history. Organize the history into sessions (say, groups of listen events with a >= 30 minute gap in between). Feed those sessions into a collaborative filtering library like Surprise[1], as a CSV of `, , 1` (1 being a rating--in this case we only have positive ratings). Then make some UI that lets people create and export playlists. e.g. I pick a couple seed songs from my listening history, then the app uses Surprise to suggest more songs. Present a list of 10 songs at a time. Click a song to add it, and have a "skip all" button that gets a new list of songs. Save these interactions as ratings--e.g. if I skip a song, that's a -1 rating for this playlist. For some percentage of the suggestions (20% by default? Make it configurable), use Last.fm's or Spotify's API to pick a new song not in your history, based on the songs in the current playlist. Also sometimes include songs that were added to the playlist previously--if you skip them, they get removed from the playlist. Then you can spend a couple minutes every week refreshing your playlists. Export the playlists to Spotify/Apple Music/whatever.

As you get more users, you can do "regular" collaborative filtering (i.e. with different users) to recommend new songs instead of relying on external APIs. There are probably lots of other things you could do too--e.g. scrape wikipedia to figure out what artists have done collaborations or something. In general I think the right approach is to build a model for artist similarity rather than individual song similarity. At recommendation time, you pick an artist and then suggest their top songs (and sometimes pick an artist already in the user's history, and suggest songs they haven't heard yet--that's even easier).

This is the simplest thing I can think of that would solve my "I love music but I listen to the same old songs everyday because I'm busy and don't want to futz around with curating my music library" problem. You wouldn't have to waste time building a crappy custom music app, and users won't have to use said crappy custom music app (speaking from personal experience...). You wouldn't have to deal with music rights or integrating with Spotify/Apple Music since you're not actually playing any music.

If you want to go further with it, you could get traction first and then launch your own streaming service or something. (Reminds me a bit of Readwise starting with just highlights and then launching their own reader recently). I think it'd be neat to make an indie streaming service--kind of like Bandcamp but with an algorithm to help you find the good stuff. Let users upload and listen to their own MP3s so it can still work with popular music. Of course it'd be nicer for users in the short term if you just made deals with the big record labels, however this would help you not end up in Spotify's position of pivoting to podcasts so you can get out of paying record labels. And then maybe in a few decades all the good music won't be on the big labels anyway :).

Anyway if anyone is remotely interested in building something like this, I'll be your first user. I really need it. Otherwise I'll probably build it myself at some point in the next year or two as a side project.

[1] http://surpriselib.com/

I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery[1]; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib[2]. It's pretty easy to play around with, just give it a csv of , , and then you can start making rating predictions. Also fastText[3] is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. to handle exploration-vs-exploitation as discussed in another thread here.

Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system[4] start using it for example).

I'm happy to chat about recommender systems also if you like, email's in my profile.

[1] https://findka.com

[2] http://surpriselib.com/

[3] https://fasttext.cc/

[4] https://essays.findka.com