5 ms·
I often find that artists who I like tend to work together (this is especially true in hip-hop and electronica, but also in rock music as well through side proj
by exelius 10y ago
I often find that artists who I like tend to work together (this is especially true in hip-hop and electronica, but also in rock music as well through side projects). I've found some of my favorite albums this way; and it's weird esoteric stuff that other people tend to like (just harder to find unless you know to look for it).
Is there an algorithm that suggests music based on the degrees of connectedness to the individuals who made the music? I discovered Kanye West long, long before he made it big because he did a guest track on a label-produced mixtape. I said "man this shit is dope" and tried to track him down, but was only able to find other guest tracks on like a Mos Def album and a Common album (this was in like 1998, 5 years before he released a solo album).
I really miss that way of finding music, because it introduced me to a lot of artists who were similar enough I would like them, but different enough that genres started to morph into one another. A good example there would be Nine Inch Nails vs. How To Destroy Angels - Trent Reznor is behind both of them, but HTDA has a much more haunting, ambient sound compared to the sharp edges and distorted beats of NIN.
- nrub 10y agoPageRank is analogous to this type of music discovery if you created a graph based on artists who have collaborated together. It'd be about the same as having a teleportation vector that always restarts the random walk back at the artist you're wanting to explore around.
- skukreti 10y agoA class I took this fall covered recommendation on attributed graphs. On of the research papers on the reading list for the topic was very interesting: it extends the matrix factorization algorithm on a bipartite graph (of users and soundtracks, for eg.) to more general graph structures (which could include soundtracks, artists). I think this exploits local connectivity between artists much better than the vanilla matrix factorization algorithm. [1] Yu, X., Ren, X., Sun Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J. (2014) Personalized entity recommendation: A heterogeneous information network approach. in J Proc. 2014 ACM Int. Conf. on Web Search and Data Mining (WSDM’14)