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Warning: this comment has little to do with the article, beyond being a rant on the approach taken by all recommendation engines I've seen. This an interesting
by johnlbevan2 9y ago
Warning: this comment has little to do with the article, beyond being a rant on the approach taken by all recommendation engines I've seen.
This an interesting approach, but the objective is similar to most recommendation engines: "Find me something similar to something I like". Sometimes that's a good requirement (e.g. when trying to queue up the next song in a playlist, it's good to have some similarity to the song you're currently listening to). However, when trying to discover new music it's generally a bad approach; since (depending how the requirement is tackled) you'll get recommendations that tend towards some median; i.e.:
- Other songs by the same artist
- Songs by artists who have collaborated with the current artist
- Popular songs (i.e. if almost everyone has a Beetles album in their playlist, getting "people who bought this also bought" recommendations for anything would list Beetles, since technically that's true; it's just uninteresting.
- Songs in the same genre
- Songs with a similar sound / structure
i.e. it tends to list things which you're likely to be aware of anyway. Also this means you'll get lots of songs with little variety between them; making your playlists monotonous.
What I'd be really interested in seeing was an engine which finds things on the peripheral; i.e. figures out the things that are likely to appeal to you because of the more unique things you're interested in; or the popular things that you dislike. That way you're likely to get a more eclectic mix of suggestions, and broaden your musical awareness. This would likely produce a lot more false positives initially, as it's expanding your taste range rather than narrowing in on some "ideal" average, so may stray into unknowns; but once you've heard and rated something in this new area, that data can quickly feedback into the algorithm and thus you learn of things you'd previously never have discovered.
- amelius 9y agoWhat could also help in this case is "collaborative filtering", [1]. [1] https://en.wikipedia.org/wiki/Collaborative_filtering https://en.wikipedia.org/wiki/Collaborative_filtering
- arnioxux 9y agoWhich just means "people similar to you who like x also like y". This does make a lot more sense than analyzing the audio of the music IMO. For example youtube does this okay and if you look for a Mazzy Star song after watching Ricky and Morty (a tv show), it will recommend other Ricky and Morty soundtracks even if the style is completely different. This isn't something you can predict with just audio data.
- emadb 9y agoI totally agree with you. Sometimes you love a song from the first listen, it bites you even if came from an artist that you don't know. My dream is a suggestion engine that "examine" the melody, the harmony, the frequencies that make the song and finds songs that are similar based on that parameters. Probably a signal analysis could help in finding why you like that songs.
- visarga 9y agoBut this article does exactly that: signal analysis with neural nets on song spectrograms. It just doesn't generate the kind of matches you want.
- hcoura 9y agoWhat you complained was exactly what I would complain about Spotify's suggestions some time ago. But as of, 3-6 months ago those daily mixes started putting some really interesting new songs that I wouldn't find otherwise. Sometimes it seems to go back to that "safe zone" but it's been such a much better experience I have been telling all my friends to try it. I really would like to know more about their process to improve the recommendation system.
- erichmond 9y agoCouldnt' agree more. Spotify seems to be solving this problem. No other rec engine I've seen is as good at finding artists I've never heard of, who I really dig
- flashman 9y ago> Popular songs (i.e. if almost everyone has a Beetles album in their playlist, getting "people who bought this also bought" recommendations for anything would list Beetles I've been learning recommendation engines by looking at peoples' Steam games libraries. One feature of the data set is that many, many people own multiple versions of Counter-Strike as well as Team Fortress 2. So "a high number people who bought [almost any game] also bought Counter-Strike: Global Operations" is a recurring problem with a naive recommender. What I've been learning how to do is weight recommendations by how 'surprising' they are, for want of a more accurate term. If 80% of people who own Game A also own Game B, but only 5% of the total population owns Game B, then we should upweight that relationship.
- johnlbevan2 9y agoNice approach; great to see others thinking about the issue (and unlike me, actually developing something that does something about it).
- SanderMak 9y agoI think 'serendipity' [1] is the most-used term in recommender systems to describe what you mean. [1] https://books.google.nl/books?id=_AfABAAAQBAJ&pg=PA258&lpg=PA258&dq=serendipity+machine+learning https://books.google.nl/books?id=_AfABAAAQBAJ&pg=PA258&lpg=P...
- bsenftner 9y agoMy problem with all music recommendation engines, and for many intellectual music aficionados, the lyrics content - what is being verbally described in the music - is what I seek and hang on for my preferred music. When I listen to my collection, the genres are all over and I don't even know them. I listen to the words and treat the music as emphasis for the words. I'll have ska, 30's jazz, hip hip, and classic rock all in the same mix and it works because the lyric content is different takes on the same things. In fact, new friends are sometimes dizzy from my music choices, and then at some point they hear the thematic concept of my mixes and get it.
- rasjani 9y agoHaving a selection that has lyrical continuation from one song into an another is very typical also in reggae. Reggae as "genre" itself is also quite varied in what goes under its label. There are also other factors that play a big weight on how good matches they are to reference material. Producer and decade make a huge difference but also what's known as "riddim" name should give clues.
- johnlbevan2 9y agoNot something I'd ever thought of (I tend to tune out the words / mostly treat them as another instrument; unless listening to something especially witty). Great suggestion / I guess this leads to the idea of needing a meta recommendation engine; i.e. some way to decide what recommendation engine best works for you; selecting from one that follows lyrical themes, another that discovers "out there" content, one for similar content, etc.
- Nimitz14 9y agoYup. Spotify is terrible at this, I religiously listen to release radar and your music of the week and the hitrate is probably 1/100?