PLAIN: An Explainable Generative Search System Enhanced by Multi-granularity Semantic Alignment

Abstract

Industrial search platforms must efficiently retrieve relevant items from billions of candidates while satisfying both query relevance and user preferences. Generative Search (GS) has emerged as a transformative paradigm that reformulates traditional indexing and matching as an autoregressive generation task. However, most existing generative search models suffer from two critical deficiencies: (1) generated Semantic IDs (SIDs) often lack explicit semantic correspondences, undermining codebook interpretability; (2) unstructured codebooks impose a fully-connected search space, forcing the generator to navigate a highly entangled decoding path. To address these limitations, we propose PLAIN, which integrates Multi-stage Codebook Construction (MCC) and Unified Generative Retrieval (UGR). MCC leverages LLM-generated taxonomy and metadata labels, applying hard assignment for closed-set taxonomy levels and soft assignment for open-set metadata levels, transforming unstructured codebooks into interpretable hierarchical topic paths. UGR operationalizes the MCC schema by employing Symmetric Context Encoders (SCE) that align both query and item representations to the structured label space via knowledge distillation and semi-supervised hierarchical quantization, enabling consistent end-to-end generative retrieval. Extensive experiments and online A/B testing in Kuaishou’s live search system demonstrate significant improvements in user engagement and content consumption.