Generative Contextual Ranking: Request-Level Global Optimization of Recommendation Slates

Modern recommender systems typically score user–item pairs independently, even though users consume recommendations as ordered slates. This mismatch prevents rankers from modeling how an item’s utility depends on earlier recommendations and can place redundant content close together, reducing diversity and degrading user experience. To solve this problem, we propose Generative Contextual Ranking (GCR), which replaces independent scoring with autoregressive, request-level slate construction. At each step, GCR selects an item conditioned on the user's history and the items already placed, capturing inter-item effects such as topical redundancy, complementarity and position dependence. GCR is a lightweight architectural change that can be inserted at any ranking stage without a full-stack rewrite. Its principal cost is the latency of autoregressive decoding, which we offset with a set of serving optimizations that improve throughput by over 10$\times$. Experiments on a large-scale short-form video platform and on a public benchmark demonstrates GCR can improve offline prediction quality and deliver statistically significant online gains.