OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender

摘要

In recommendation systems, scaling up feature-interaction modules (e.g., Wukong, RankMixer) or user-behavior sequence modules (e.g., LONGER) has achieved notable success. However, these efforts typically proceed on separate tracks, which not only hinders bidirectional information exchange but also prevents unified optimization and scaling. In this paper, we propose Model, a unified Transformer backbone that simultaneously performs user-behavior sequence modeling and feature interaction. Model employs a unified tokenizer to convert both sequential and non-sequential attributes into a single token sequence. The stacked Model blocks share parameters across similar sequential tokens while assigning token-specific parameters to non-sequential tokens. Through causal attention and cross-request KV caching, Model enables precomputation and caching of intermediate representations, significantly reducing computational costs during both training and inference. Experimental results on industrial-scale datasets demonstrate that Model scales efficiently with increasing parameters, consistently outperforms strong baselines, and yields a 5.68% lift in per-user GMV in online A/B tests.