Sequential Recommendations for Search Reranking at Bloomreach

Sequential recommendation models based on self-attention, such as SASRec, are a natural fit for e‑commerce search: they can condition on a user’s full interaction history and adapt as intent evolves. At Bloomreach, we use a SASRec‑style model to re‑rank the top 200 results from an existing learning‑to‑rank (LTR) model for both keyword and category search, rather than replacing the ranking stack outright. This talk shares what we learned while turning that idea into a production system across a wide variety of customers.