SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress
This work aims to improve the adaptability and versatility of recommender systems for e-commerce platforms like AliExpress by enabling them to handle various recommendation tasks through instruction-following.
This paper introduces SIGMA, a generative multi-task recommender system deployed at AliExpress. It addresses the limitations of interaction-driven next-item prediction by using semantic grounding and instruction-following to adapt to diverse recommendation tasks and business requirements.
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.