Training and Deploying Multi-Stage Recommender Systems
Ronay Ak, Benedikt Schifferer, Sara Rabhi, Gabriel de Souza Pereira Moreira · 2022
Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, and an ordering stage. These pipelines need to be carefully deployed as a set, requiring coordination during their development and deployment. Data scientists, ML engineers, and researchers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines.