With the rapid evolution of Large Language Models, generative recommendation is gradually reshaping the paradigm of recommender systems. However, most existing methods are still confined to the interaction-driven next-item prediction paradigm, failing to rapidly adapt to evolving trends or address diverse recommendation tasks along with business-specific requirements in real-world scenarios. To this end, we present SIGMA, a Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress. Specifically, we first ground item entities in general semantics via a unified latent space capturing both semantic and collaborative relations. Building upon this, we develop a hybrid item tokenization method for precise modeling and efficient generation. Moreover, we construct a large-scale multi-task SFT dataset to empower SIGMA to fulfill various recommendation demands via instruction-following. Finally, we design a three-step item generation procedure integrated with an adaptive probabilistic fusion mechanism to calibrate the output distributions based on task-specific requirements for recommendation accuracy and diversity. Extensive offline experiments and online A/B tests demonstrate the effectiveness of SIGMA.

翻译：随着大型语言模型的快速发展，生成式推荐正逐步重塑推荐系统的范式。然而，现有方法大多仍局限于交互驱动的下一项预测范式，难以快速适应不断变化的趋势，也无法满足现实场景中多样化的推荐任务及业务特定需求。为此，我们提出SIGMA，即速卖通平台上基于语义的指令驱动生成式多任务推荐系统。具体而言，我们首先通过一个统一潜空间捕获语义关系与协同关系，将物品实体锚定于通用语义中。在此基础上，我们开发了一种混合物品标记化方法，以实现精确建模与高效生成。此外，我们构建了一个大规模多任务监督微调数据集，使SIGMA能够通过指令跟随机制满足多样化的推荐需求。最后，我们设计了一个三步物品生成流程，并结合自适应概率融合机制，根据任务特定需求校准输出分布，以平衡推荐准确性与多样性。大量离线实验与在线A/B测试验证了SIGMA的有效性。