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Practitioners who wish to build real-world applications that rely on rankingmodels, need to decide which modelling paradigm to follow. This is not an easychoice to make, as the research literature on this topic has been shifting inrecent years. In particular, whilst Gradient Boosted Decision Trees (GBDTs)have reigned supreme for more than a decade, the flexibility of neural networkshas allowed them to catch up, and recent works report accuracy metrics that areon par. Nevertheless, practical systems require considerations beyond mereaccuracy metrics to decide on a modelling approach.  This work describes our experiences in balancing some of the trade-offs thatarise, presenting a case study on a short-video recommendation application. Wehighlight (1) neural networks' ability to handle large training data size,user- and item-embeddings allows for more accurate models than GBDTs in thissetting, and (2) because GBDTs are less reliant on specialised hardware, theycan provide an equally accurate model at a lower cost. We believe thesefindings are of relevance to researchers in both academia and industry, andhope they can inspire practitioners who need to make similar modelling choicesin the future.

On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat