OneChord improves web product recommendations with faster ranking
OneChord: Distill What You Rank, Recommend in One Pass
Information Retrieval
Summary
Finding the best products online quickly is hard because recommendation models have only a little time to pick top items. The authors introduce OneChord, a method that learns to combine different signals about items in one step instead of multiple passes, making rankings more accurate and faster. They tested this approach on six Amazon product sets and found it outperforms other methods in both speed and quality.
What this means in practice
- •For ecommerce platform engineers: Improve product ranking speed and accuracy in online stores within tight computational limits using OneChord’s single-pass fusion method.
- •For search engine developers: Enhance search result recommendations by integrating multi-signal ranking into one efficient inference step, reducing latency while improving relevance.
Authors
Zesheng Li, Chengchang Pan, Honggang Qi
Abstract
Accurate product discovery on the Web requires recommendation models that rank catalog candidates within tight inference budgets. In semantic-ID recommendation, deployment can fuse code probabilities with item similarities, making the final ranking depend on their normalization and relative weights. This motivates a direct training principle: \emph{distill what you rank}. We present OneChord, which uses one learned, catalog-normalized fusion for next-item supervision, refinement distillation, and inference. A shared parallel decoder produces joint SID evidence and item-matching scores. During training, a frozen denoiser supplies legal partial SIDs; the student evaluates these states to construct refined fused targets. Matching direct and refined predictions on shared hard candidates transfers refinement into one-pass recommendation. We characterize the fusion weights induced by candidate conditioning and establish sufficient conditions for preserving the refined predictor's full-catalog Top-$K$ selection. Across six Amazon datasets, OneChord achieves the highest Recall@5 and NDCG@5 among the compared methods on every dataset, with average relative gains of 6.30\% and 7.12\% over the strongest baseline. It also achieves a 1.04--1.18$\times$ full-test generation speedup over the fastest baseline. OneChord makes the served ranking distribution the common object of recommendation learning and distillation.