SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress

• URL: http://arxiv.org/abs/2602.22913v1
• Date: Thu, 26 Feb 2026 12:00:46 GMT
• 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,
• Abstract summary: We present SIGMA, a Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender.<n>We first ground item entities in general semantics via a unified latent space capturing both semantic and collaborative relations.<n>We construct a large-scale multi-task SFT dataset to empower SIGMA to fulfill various recommendation demands via instruction-following.
• Score: 17.904783443841726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals [17.608491612845306]
  Sequential recommender systems rank relevant items by modeling a user's interaction history and computing the inner product between the resulting user representation and stored item embeddings.<n>To avoid the significant memory overhead of storing large item sets, the generative recommendation paradigm instead models each item as a series of discrete semantic codes.<n>These methods have yet to surpass traditional sequential recommenders on large item sets, limiting their adoption in the very scenarios they were designed to address.<n>We propose MSCGRec, a Multimodal Semantic and Collaborative Generative Recommender.
  arXiv  Detail & Related papers  (2026-02-03T16:39:35Z)
• RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment [23.738860191046538]
  We propose RecGOAT, a novel yet simple dual semantic alignment framework for multimodal recommendation.<n>We show that RecGOAT achieves state-of-the-art performance, empirically validating our theoretical insights.
  arXiv  Detail & Related papers  (2026-01-31T11:58:38Z)
• Multi-Aspect Cross-modal Quantization for Generative Recommendation [27.92632297542123]
  We propose Multi-Aspect Cross-modal quantization for generative Recommendation (MACRec)<n>We first introduce cross-modal quantization during the ID learning process, which effectively reduces conflict rates.<n>We also incorporate multi-aspect cross-modal alignments, including the implicit and explicit alignments.
  arXiv  Detail & Related papers  (2025-11-19T04:55:14Z)
• Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models [99.85131798240808]
  We introduce a novel generative framework called textitGuided Topology Diffusion (GTD)<n>Inspired by conditional discrete graph diffusion models, GTD formulates topology synthesis as an iterative construction process.<n>At each step, the generation is steered by a lightweight proxy model that predicts multi-objective rewards.<n>Experiments show that GTD can generate highly task-adaptive, sparse, and efficient communication topologies.
  arXiv  Detail & Related papers  (2025-10-09T05:28:28Z)
• A Unified Multi-Task Learning Framework for Generative Auto-Bidding with Validation-Aligned Optimization [51.27959658504722]
  Multi-task learning offers a principled framework to train these tasks jointly through shared representations.<n>Existing multi-task optimization strategies are primarily guided by training dynamics and often generalize poorly in volatile bidding environments.<n>We present Validation-Aligned Multi-task Optimization (VAMO), which adaptively assigns task weights based on the alignment between per-task training gradients and a held-out validation gradient.
  arXiv  Detail & Related papers  (2025-10-09T03:59:51Z)
• ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction [84.90394416593624]
  Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions.<n>Existing simulation-based data generation methods rely heavily on costly autoregressive interactions between multiple agents.<n>We propose a novel Non-Autoregressive Iterative Generation framework, called ToolACE-MT, for constructing high-quality multi-turn agentic dialogues.
  arXiv  Detail & Related papers  (2025-08-18T07:38:23Z)
• Towards Agentic AI for Multimodal-Guided Video Object Segmentation [14.877182670778284]
  Referring-based Video Object is a multimodal problem that requires producing fine-grained segmentation results guided by external cues.<n>Recent advances in vision-language foundation models open a promising direction toward training-free approaches.<n>We propose Multi-Modal Agent, a novel agentic system designed to solve this task in a more flexible and adaptive manner.
  arXiv  Detail & Related papers  (2025-08-14T12:11:15Z)
• A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao [20.11584617315975]
  We propose a novel end-to-end generative re-ranking model named Sequential Ordered Regression Transformer-Generator (SORT-Gen) for the less-studied list-level multi-objective optimization problem.<n>SORT-Gen has been successfully deployed in multiple scenarios of Taobao App, serving for a vast number of users.
  arXiv  Detail & Related papers  (2025-05-12T03:01:14Z)
• Pre-training Generative Recommender with Multi-Identifier Item Tokenization [78.87007819266957]
  We propose MTGRec to augment token sequence data for Generative Recommender pre-training.<n>Our approach involves two key innovations: multi-identifier item tokenization and curriculum recommender pre-training.<n>Extensive experiments on three public benchmark datasets demonstrate that MTGRec significantly outperforms both traditional and generative recommendation baselines.
  arXiv  Detail & Related papers  (2025-04-06T08:03:03Z)
• Meta-Task Prompting Elicits Embeddings from Large Language Models [54.757445048329735]
  We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation. We generate high-quality sentence embeddings from Large Language Models without the need for model fine-tuning. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.
  arXiv  Detail & Related papers  (2024-02-28T16:35:52Z)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.