面向本地生活服务推荐的、基于大语言模型驱动的生成式解缠的可解释表示

中文摘要 AI 辅助

尽管大语言模型（LLMs）通过语义ID（SID）建模推动了基于ID的推荐系统的发展，但现有的SID生成框架大多遵循“单一表示后量化”的范式。这种设计面临两个瓶颈：语义纠缠将地理、品牌、类别等异质属性混合，导致量化过程中出现信息丢失、SID质量低下以及严重的碰撞问题；此外，黑箱式表示学习无法为SID位置提供明确的属性语义，也无法提供清晰的地理或语义含义。这些限制削弱了检索的可靠性，以及对SID生成进行诊断或控制的能力。我们提出面向本地生活服务推荐的、基于大语言模型驱动的生成式解缠的可解释表示（LGRID）。LGRID引入了“编码->解缠->对齐->量化”的生成式解缠范式。它首先采用联合LLM编码来保留跨属性的地理-语义依赖关系，而非独立编码各字段。随后，结构化解缠模块将隐藏状态路由到与地理和语义因子属性对齐的槽位。协同对齐学习使这些槽位既能生成式解码，又能为检索任务提供判别能力，而双流残差量化则将两个流分别离散化为具有明确属性对应关系的紧凑SID。该设计生成了可解释的SID，其位置基于物品属性和本地服务语义。在快手（Kuaishou）和Foursquare数据集上的实验表明，LGRID始终优于强大的SID基线方法，实现了最高5.44%的相对AUC提升；同时，它对粗粒度地理字段的属性解码准确率超过99%，并将全SID碰撞率降至39.9%，而LGSID的碰撞率为97.0%。

英文摘要

While large language models (LLMs) have advanced ID-based recommendation through Semantic ID (SID) modeling, existing SID generation frameworks largely follow a single-representation-then-quantization paradigm. This design faces two bottlenecks: semantic entanglement mixes heterogeneous attributes, such as geography, brand, and category, causing information loss during quantization, low-quality SIDs, and severe collisions; moreover, black-box representation learning provides neither explicit attribute semantics nor clear geographic or semantic meanings for SID positions. These limitations weaken both retrieval reliability and the ability to diagnose or control SID generation. We propose Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation (LGRID). LGRID introduces a generative disentanglement paradigm through an Encode -> Disentangle -> Align -> Quantize pipeline. It first uses joint LLM encoding to preserve cross-attribute geographic-semantic dependencies, rather than encoding fields independently. A Structured Disentangled Block then routes hidden states into attribute-aligned slots for geographic and semantic factors. Synergistic Alignment Learning makes these slots both generatively decodable and discriminative for retrieval, while Dual-Stream Residual Quantization separately discretizes the two streams into compact SIDs with explicit attribute correspondence. This design yields interpretable SIDs with positions grounded in item attributes and local-service semantics. Experiments on Kuaishou and Foursquare show that LGRID consistently outperforms strong SID baselines, achieving up to a 5.44 percent relative AUC gain. It also achieves over 99 percent attribute-decoding accuracy for coarse geographic fields and reduces the full-SID collision rate to 39.9 percent, compared with 97.0 percent for LGSID.

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。