This tries to use the approach in Blondel et al and Zhu, 16 for using a weight/mask matrix to ignore missing values while doing the Lee & Seung multiplicative update. The latter implementation can be found in a git repo here
It initially seemed to work well for some data where out-of-sample performance increased, but for other data it performed horrendously where predictions weren't even on the same scale as the user ratings. Given how finicky it seems to be, I've unmerged it from master and I'm leaving it in this branch/PR for posterity.
This tries to use the approach in Blondel et al and Zhu, 16 for using a weight/mask matrix to ignore missing values while doing the Lee & Seung multiplicative update. The latter implementation can be found in a git repo here
It initially seemed to work well for some data where out-of-sample performance increased, but for other data it performed horrendously where predictions weren't even on the same scale as the user ratings. Given how finicky it seems to be, I've unmerged it from master and I'm leaving it in this branch/PR for posterity.