Generative Retrieval (GR) introduces a new information retrieval paradigm that directly generates unique document identifiers (DocIDs). The key challenge of GR lies in creating effective yet discrete DocIDs that preserve semantic relevance for similar documents while differentiating dissimilar ones. However, existing methods generate DocIDs solely based on the textual content of documents, which may result in DocIDs with weak semantic connections for similar documents due to variations in expression. Therefore, we propose using queries as a bridge to connect documents with varying relevance levels for learning improved DocIDs. In this paper, we propose **M**ulti-l**E**vel **R**elevance document identifier learning for **G**enerative r**E**trieval (MERGE), a novel approach that utilizes multi-level document relevance to learn high-quality DocIDs. MERGE incorporates three modules: a multi-relevance query-document alignment module to effectively align document representations with related queries, an outer-level contrastive learning module to capture binary-level relevance, and an inner-level multi-level relevance learning module to distinguish documents with different relevance levels. Our approach encodes rich hierarchical semantic information and maintains uniqueness across documents. Experimental results on real-world multilingual e-commerce search datasets demonstrate that MERGE significantly outperforms existing methods, underscoring its effectiveness. The source code is available at <https://github.com/zhangfw123/MERGE>.

Food delivery search aims to quickly retrieve deliverable items that meet users’ needs, typically requiring faster and more accurate query understanding compared to traditional e-commerce search. Generative retrieval (GR), an emerging search paradigm, harnesses the advanced query understanding capabilities of large language models (LLMs) to enhance the retrieval of results for complex and long-tail queries in food delivery search scenarios. However, there are still challenges in deploying GR to online scenarios: 1) **the large scale of items**; 2) **latency constraints unmet by LLM inference in online retrieval**; and 3) **strong location-based service restrictions on generated items**. To explore the application of GR in food delivery search, we optimize both offline training and online deployment, proposing **Hier**archical semantic representation enhancement for **G**enerative **R**etrieval (HierGR). Specifically, for the generation of semantic IDs, we propose an optimization method that refines the residual quantization process to generate hierarchically semantic IDs for items. Additionally, to successfully deploy on a well-known food delivery platform, we utilize the query cache mechanism and integrate the GR model with the online dense retrieval model to fulfill real-world search requirements. Online A/B testing results show that our proposed method increases **the number of online orders by 0.68%** for complex search intents. The source code is available at https://github.com/zhangfw123/HierGR.