Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation
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 I...
| Authors | Long Zhang, Hao Jiang, Sheng Yu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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