# Learning RecSys through Papers Vol II- The How, What, and Why of In-Batch Negatives

Consider the retrieval problem in a recommendation system. One way to model the problem is to create a big classification model predicting the next item a user will click or watch. This basic approach could be extended to large catalogs via a sampled softmax loss, as discussed in our last post. Naturally, the resulting dataset will only contain positive examples (i.e., what was clicked). In this post, we explore a more optimized version of this approach for more complex models via in-batch negatives. We derive the LogQ Correction to improve the estimate of the gradient and present code in PyTorch to implement the method, building on the previous blog post.