How are collaborative-filtering and topic-modeling different and how are they the same?

Question

related to: Simple Python implementation of collaborative topic modeling?

I'm trying to grasp the fundamental differences and the fundamental parts that are the same in collaborative-filtering and topic-modeling. Both seems very much alike to me: trying to look for a latent dimension which can compactly predict which user would like which movie, or which document would contain which word?

Can you shed some light or send me to sources that will clarify that?

Thanks!

Answer 1

I think this paper is your best bet:

https://www.cs.princeton.edu/~blei/papers/WangBlei2011.pdf

It talks about combining collaborative filtering and topic modeling (two really distinct things). There is maybe some resemblance if you look especially at probabilistic matrix factorization for collaborative filtering and probabilistic topic modeling, in the way the solutions is generated, but that is still rather limited.

From your question it is not clear whether you're wondering about topic modeling or collaborative topic modeling. Nonetheless, the paper I mentioned gives some background on collaborative filtering (through matrix factorization), some background on probabilistic topic modeling and then:

COLLABORATIVE TOPIC REGRESSION (CTR), CTR combines traditional traditional collaborative filtering with topic modeling.

Just realized that this paper is already referenced in the question you are linking to, so let me share another great resource, this article in the NYT which is less math-heavy

http://open.blogs.nytimes.com/2015/08/11/building-the-next-new-york-times-recommendation-engine/

Where they describe, how they actually implemented the approach from the paper mentioned above.

On the contrary for more details on topic modeling I'd suggest diving into resources on this page:

https://www.cs.princeton.edu/~blei/topicmodeling.html

and this paper for matrix factorization for collaborative filtering:

https://datajobs.com/data-science-repo/Recommender-Systems-%5BNetflix%5D.pdf