AI 中文总结
研究针对生成式推荐存在的结构差距问题，提出BARGE方法，通过项目上下文感知注意力恢复项目级结构，利用分层路径重排和双路径解码抑制语义漂移，实验表明该方法推荐性能优越，在工业规模推荐中有实用价值。
AI 中文摘要
生成式推荐（GR）已成为序列推荐的新范式，其中一项代表性工作通过残差量化将项目编码为分层语义ID，并逐个预测ID令牌。然而，这种生成式公式在推荐任务中仍存在结构差距：将多令牌ID扁平化为单个序列会破坏项目级结构，并且在分层码本上训练和推理之间的不一致会导致语义漂移。为弥合这两个差距，我们提出了BARGE，它在编码期间采用项目上下文感知注意力（ICA）来恢复项目级结构，并在解码期间通过分层路径重排（HPR）和双路径解码（DPD）从两个互补角度抑制语义漂移。在公共基准和大规模离线测试上的大量实验和分析研究表明，BARGE实现了卓越的推荐性能。在腾讯平台上的在线A/B测试在点击率、点击独立访客数和总阅读时间方面分别提高了0.60%、1.34%和1.70%，证实了BARGE在工业规模推荐中的实用价值。
英文摘要
Generative Recommendation (GR) has emerged as a new paradigm for sequential recommendation, in which a representative line of work encodes items into hierarchical semantic IDs via residual quantization and predicts the IDs token by token. However, this generative formulation still exhibits structural gaps with respect to the recommendation task: flattening multi-token IDs into a single sequence destroys item-level structure, and the inconsistency between training and inference over a hierarchical codebook gives rise to semantic drift. To bridge these two gaps, we propose BARGE, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding, and Hierarchical Path Reranking (HPR) together with Dual-Path Decoding (DPD) to suppress semantic drift from two complementary angles during decoding. Extensive experiments and analytical studies on public benchmarks and a large-scale offline test demonstrate that BARGE achieves superior recommendation performance. An online A/B test on a Tencent platform yields improvements of 0.60% in click-through rate, 1.34% in click unique visitors, and 1.70% in total reading time, confirming the practical value of BARGE in industrial-scale recommendation.
Comments14 pages, 15 figures
