解决生成式推荐的结构缺失与语义漂移问题，提升推荐效果
Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation
- 用上下文感知注意力恢复物品层级结构
- 通过路径重排序和双路径解码抑制语义漂移
- 在腾讯平台上线测试，点击率等指标显著提升
生成式推荐作为序列推荐的新范式，通过残差量化将物品编码为分层语义ID，并逐个预测。然而该方法存在两大结构性缺陷：将多标记ID扁平化为单序列会破坏物品级结构；训练与推理间分层码本不一致导致语义漂移。为此，本文提出BARGE，采用物品上下文感知注意力（ICA）在编码阶段恢复物品级结构；在解码阶段结合分层路径重排序（HPR）与双路径解码（DPD），从两个互补角度抑制语义漂移。在公开数据集及大规模离线测试中，BARGE表现更优。在腾讯平台的在线A/B测试显示，点击率提升0.60%，点击独立访客增加1.34%，总阅读时长增长1.70%，验证了其工业级实用性。
原文摘要 · Abstract (English)
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.
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