Recommendation systems and search have historically drawn inspiration from language modeling. For example, the adoption of Word2vec to learn item embeddings (for embedding-based retrieval), and using GRUs, Transformer, and BERT to predict the next best item (for ranking). The current paradigm of large language models is no different. Here, we’ll discuss how industrial search and recommendation systems have evolved over the past year or so and cover model architectures, data generation, training ...