解决生成式推荐中语义与协同信号跨阶段解耦问题
Addressing Cross-Stage Decoupling of Semantic and Collaborative Signals in Generative Recommendation
浏览论文内容
中文总结 AI 辅助
针对生成式推荐中语义与协同信号跨阶段解耦问题，提出SCRec框架，通过协同增强分词、语义引导生成和流形对齐实现双向信息补充，提升语义连贯性与推荐准确性。
中文摘要 AI 辅助
生成式推荐通过将物品编码为语义令牌，将序列推荐重构为自回归生成，从而提升扩展能力和跨域泛化能力。然而，现有的生成式推荐系统通常采用两阶段流程，其中物品分词主要由文本语义主导，对协同信号和交互相似性的纳入有限，导致代码分配与下游生成不一致。相反，生成阶段往往忽视原始语义信息，因为代码序列基于交互数据重新嵌入。这种跨阶段信息解耦限制了语义连贯性和推荐准确性。为解决此问题，我们提出SCRec，一个通过双向信息补充增强跨阶段连贯性的通用框架。具体而言，我们引入（i）协同增强分词，将文本化的协同信号显式注入语义分词中，无需引入额外对齐任务；（ii）语义引导生成，在生成阶段使用可学习代码嵌入动态重新校准语义先验；以及（iii）流形对齐，以协调离散码本索引嵌入空间与稠密连续语义空间之间的几何不匹配。这些相互关联的组件构成一个通用框架，对齐语义和协同信号并增强跨阶段信息连贯性，且仅需极少的额外训练和推理成本。大量实验证明了我们提出框架的有效性、鲁棒性和泛化性。
英文摘要
Generative recommendation reformulates sequential recommendation as autoregressive generation by encoding items into semantic tokens, enabling improved scaling capability and cross-domain generalization. However, existing generative recommender systems typically follow a two-stage pipeline, where item tokenization is largely dominated by textual semantics with limited incorporation of collaborative signals and interaction similarity, leading to code assignments that are misaligned with downstream generation. Conversely, the generation stage tends to overlook the original semantic information, as the code sequences are re-embedded based on interaction data. This cross-stage information decoupling limits semantic coherence and recommendation accuracy. To address this issue, we propose SCRec, a general framework that enhances cross-stage coherence through bidirectional information supplementation. Specifically, we introduce (i) collaborative-enhanced tokenization to explicitly inject textualized collaborative signals into semantic tokenization, without introducing additional alignment task, (ii) semantic-guided generation to dynamically recalibrate semantic priors with learnable code embeddings in generation stage, and (iii) manifold alignment to reconcile the geometric mismatch between the embedding space of discrete codebook indices and the dense continuous semantic space. These interrelated components form a general framework that aligns semantic and collaborative signals and enhances cross-stage information coherence, with minimal additional training and inference costs. Extensive experiments demonstrate the effectiveness, robustness, and generalizability of our proposed framework.
发表机构
- Kuaishou Technology（快手科技）
机构由 AI 辅助整理，请以论文原文为准。
