[kahnjw]

Aren't you sort of glossing over the fact that he is in high up machine learning position at a company that specializes in recommender systems? Doesn't that by itself increase the likelihood that he deeply understands implicit and explicit matrix factorization?

I am a good ways through my masters (second CS degree, first specializing in ML), and the more I learn, the more I realize that on any given topic, there is no guarantee the PhD in the room has the most expertise. Machine learning is a broad field that contains many subfields, methodologies, and many applications. It is a bit like computer systems or software engineering: nobody knows it all, people who are experts have intimate knowledge of a specific subset of the field. Of course, you can more around over time, but it takes years to build up expertise in even two or three subfields of machine learning.

Side note: sounds like we do similar work. I work at Vevo, also do a lot of matrix factorization to learn latent factors of items such as artists, videos, etc.

[achompas]

> Aren't you sort of glossing over the fact that he is in high up machine learning position at a company that specializes in recommender systems? Doesn't that by itself increase the likelihood that he deeply understands implicit and explicit matrix factorization?

Sure thing, but someone in that position needs years of experience in recommender systems, as well as working with researchers.

Folks are hanging on to the PhD part of my claim, instead of the "PhD or experience" part. The fact is, a PhD + prior industry work means the person is close to a decade of relevant background, grad degree or not. They will unstick a co-worker far faster than an experienced backend developer with, say, a year of Keras experience.

> Side note: sounds like we do similar work. I work at Vevo, also do a lot of matrix factorization to learn latent factors of items such as artists, videos, etc.

Seems like it! Email me if you'd like to chat some more offline (it's in my profile).