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[2602.22913] SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
Summary
The paper presents SIGMA, a novel generative multi-task recommender system developed for AliExpress, utilizing semantic grounding and instruction-driven methodologies to enhance recommendation accuracy and adaptability.
Why It Matters
As e-commerce platforms increasingly rely on sophisticated recommendation systems, SIGMA addresses the limitations of traditional models by integrating semantic understanding and multi-task capabilities, thus improving user experience and business outcomes. This innovation is crucial for adapting to rapidly changing consumer preferences and diverse recommendation needs.
Key Takeaways
- SIGMA utilizes a unified latent space for capturing semantic and collaborative relations.
- The system employs a hybrid item tokenization method for improved modeling and generation.
- A large-scale multi-task dataset supports SIGMA in meeting various recommendation demands.
- The three-step generation process enhances output accuracy and diversity.
- Extensive testing shows SIGMA's effectiveness in real-world applications.
Computer Science > Information Retrieval arXiv:2602.22913 (cs) [Submitted on 26 Feb 2026] Title:SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress Authors:Yang Yu, Lei Kou, Huaikuan Yi, Bin Chen, Yayu Cao, Lei Shen, Chao Zhang, Bing Wang, Xiaoyi Zeng View a PDF of the paper titled SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress, by Yang Yu and 8 other authors View PDF HTML (experimental) Abstract: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 proba...
