ARTM vs. LDA: An SVD extension case study

Abstract

In this work, we compare two extensions of two different topic models for the same problem of recommending full-Text items: previously developed SVD-LDA and its counterpart SVD-ARTM based on additive regularization. We show that ARTM naturally leads to the inference algorithm that has to be painstakingly developed for LDA.