FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets

• URL: http://arxiv.org/abs/2509.20904v2
• Date: Fri, 26 Sep 2025 00:53:31 GMT
• Title: FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets
• Authors: Kairui Fu, Tao Zhang, Shuwen Xiao, Ziyang Wang, Xinming Zhang, Chenchi Zhang, Yuliang Yan, Junjun Zheng, Yu Li, Zhihong Chen, Jian Wu, Xiangheng Kong, Shengyu Zhang, Kun Kuang, Yuning Jiang, Bo Zheng,
• Abstract summary: FORGE is a benchmark for FOrming semantic identifieR in Generative rEtrieval with industrial datasets.<n>For real-world applications, FORGE introduces an offline pretraining schema that reduces online convergence by half.
• Score: 64.51403245281547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic identifiers (SIDs) have gained increasing attention in generative retrieval (GR) due to their meaningful semantic discriminability. However, current research on SIDs faces three main challenges: (1) the absence of large-scale public datasets with multimodal features, (2) limited investigation into optimization strategies for SID generation, which typically rely on costly GR training for evaluation, and (3) slow online convergence in industrial deployment. To address these challenges, we propose FORGE, a comprehensive benchmark for FOrming semantic identifieR in Generative rEtrieval with industrial datasets. Specifically, FORGE is equipped with a dataset comprising 14 billion user interactions and multimodal features of 250 million items sampled from Taobao, one of the biggest e-commerce platforms in China. Leveraging this dataset, FORGE explores several optimizations to enhance the SID construction and validates their effectiveness via offline experiments across different settings and tasks. Further online analysis conducted on the "Guess You Like" section of Taobao's homepage shows a 0.35% increase in transaction count, highlighting the practical impact of our method. Regarding the expensive SID validation accompanied by the full training of GRs, we propose two novel metrics of SID that correlate positively with recommendation performance, enabling convenient evaluations without any GR training. For real-world applications, FORGE introduces an offline pretraining schema that reduces online convergence by half. The code and data are available at https://github.com/selous123/al_sid.

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