Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation

2026-07-30

AI Summary

Existing large language models (LLMs) generate semantic IDs that suffer from semantic entanglement and opaque representations, leading to information loss, high ID collision rates, and poor interpretability. To address these issues, this work proposes LGRID, a novel generative disentanglement framework that introduces an “encode → disentangle → align → quantize” pipeline. By jointly leveraging LLM-based encoding to preserve cross-attribute dependencies and routing latent states into attribute-aligned slots, LGRID explicitly disentangles geographic and semantic factors. This design ensures that positions within the semantic ID directly correspond to interpretable item attributes, thereby enhancing explainability, diagnosability, and retrieval effectiveness. Experiments demonstrate that LGRID achieves up to a 5.44% AUC improvement on Kuaishou and Foursquare datasets, exceeds 99% decoding accuracy for coarse-grained geographic fields, and reduces overall semantic ID collision rates to 39.9%.

Abstract

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.

Problem

• Semantic ID
• representation disentanglement
• local-life service recommendation
• attribute entanglement
• interpretable representation

Innovation

• Generative Disentanglement
• Semantic ID
• LLM-driven Representation
• Interpretable Recommendation
• Dual-Stream Quantization