Research talk: DeepXML: A deep extreme classification framework for recommending millions of items

Speaker: Deepak Saini, Research Software Development Engineer, Microsoft Research India Extreme classification provides a formulation for large-scale ranking and recommendation problems by treating each item to be ranked or recommended as a separate label in a multi-label classification problem. Scalability and accuracy are well-recognized challenges in deep extreme classification where the objective is to train feature architectures like BERT, GPT-3 jointly with the classifiers for items. This talk will introduce the DeepXML framework that addresses these challenges by decomposing the deep extreme multi-label learning task into four simpler sub-tasks, each of which can be trained accurately and efficiently. Choosing different components for the four sub-tasks allows DeepXML to generate a family of accurate and scalable algorithms geared towards different scenarios. In particular, algorithms derived from the DeepXML framework can be 10–30 percent more accurate and up to 3–97x faster to train than leadi