Before revision:  Standard deep candidate generation models rely on sophisticated sampling techniques to approximately conduct maximum likelihood estimation of user-item interactions, following the advances in the language modelingcommunity .
After revision:  Standard approaches adopt maximum likelihood estimation (MLE) and rely on sampling to ensure scalability, which reduces DCG to a task similar to language modeling .