Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· 0 citations· 22 references
Computer Science
TL;DR
RedGR is proposed, a generative retrieval model that unifies the modeling of multiple complex retrieval tasks and has been fully depolyed in the homefeed recommendation scenario of RedNote, serving hundreds of millions of users every day.
Abstract
Recently, the generative retrieval paradigm has emerged as a transformative framework that significantly enhances the efficiency of large-scale industrial recommendation systems. This innovative approach systematically maps items to meaningful semantic identifiers (SIDs) and employs advanced sequence generation techniques to construct high-quality candidate sets, thereby enabling more accurate modeling of users' evolving interests and behavioral patterns. Nevertheless, two critical challenges remain inadequately addressed in current research: (1) Existing methodologies predominantly focus on modeling a single task such as predicting users' click behavior, while overlooking other tasks including predicting users' dwell-time and engagement behaviors, which are very important for video/content recommendation at the same time. The independent modeling of each task inevitably results in substantial computational overhead, thereby raising the pivotal question of whether the sophisticated multi-task learning capabilities inherent in LLMs can be effectively leveraged to achieve unified and efficient multi-task learning for generative retrieval. (2) The mapping mechanism from SIDs to concrete items requires substantial refinement to ensure precise and reliable retrieval performance. To tackle these issues, we propose RedGR, a generative retrieval model that unifies the modeling of multiple complex retrieval tasks. RedGR first applies the RQ-Kmeans algorithm to map items into SIDs, and then conducts pre-training on large-scale user behavior datasets to learn general knowledge. Then the RedGR model is finetuned on multi-task retrieval data with a unique instruction prompt for each task. This enables RedGR to generate the corresponding set of SIDs for each task. And the union of all sets of SIDs is the multi-task retrieval result. Finally, the Swing algorithm incorporates explicit, high-quality collaborative signals to strengthen the mapping from SIDs to specific items, thereby facilitating efficient retrieval of high-quality items. RedGR has been fully depolyed in the homefeed recommendation scenario of RedNote,serving hundreds of millions of users every day. Online A/B test results show a 0.178% increase in pagetime, a 0.734% increase in average user engagement, and a 0.076% growth in homefeed active users (FAU). These metrics collectively validate the superior performance of RedGR's unified retrieval modeling approach in complex multi-task scenarios.
Generative recommendation has recently emerged as a promising paradigm that reformulates retrieval as autoregressive generation over semantic identifiers (SIDs), achieving strong performance and drawing increasing attention as an alternative to matching. Despite this progress, SIDs are typically frozen by a content-based tokenizer before the recommender is trained, leaving a persistent gap between what best reconstructs an item's content and what a recommender can predict from user behavior. Recent end-to-end methods close this gap by jointly training the tokenizer and the recommender, but coupling the two destabilizes the identifier space and requires a second learnable model, alignment losses, and usually a delicate alternating-optimization schedule. To address this issue, we propose Grevo, a unified Generative recommendation framework with evolutionary item indexing, which treats the SID assignment itself as an evolvable discrete variable that adapts to behavioral feedback rather than as a tokenizer to be trained. Grevo builds on a single multitask recommender that unifies a behavioral SID generation task and a semantic SID grounding task, letting the recommender absorb the tokenizer's role. Through evolutionary item indexing, Grevo then uses the trained recommender itself as a posterior evaluator to reassign a budgeted set of high-risk identifiers under a fixed vocabulary and length. Together, these components turn index construction into a stable, feedback-driven search that requires no second learnable model, no alignment losses, and no alternating-optimization schedule. Extensive experiments on multiple real-world datasets demonstrate that Grevo consistently outperforms state-of-the-art generative recommendation methods.
Huanjie Wang, Liwei Guan, Zekai Sun et al.· 0 citations
The design and launch of SnapLGR, an LLM-based generative retrieval system for short-video recommendation at Snapchat shows that successful production SnapLGR requires joint design across representation learning, vocabulary grounding, and efficient training and serving.
Liam Collins, Jiwen Ren, Donald Loveland et al.· 0 citations
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-tower retrieval architecture by adapting LLMs as semantic representation backbones rather than generative engines. We introduce an LLM-native two-tower framework engineered for high-throughput, large-scale retrieval. Our architecture introduces several key innovations: a shared LLM encoder for joint user-item modeling, End-Of-Sentence (EOS) token pooling for compact sequence embedding, cross-dataset transfer learning, knowledge distillation from powerful cross-encoder teachers, and latent reasoning within the user tower. Extensive evaluation across three public benchmarks demonstrates that cross-encoder architecture outperforms current state-of-the-art (SoTA) models, while the efficient two-tower student achieves SoTA-comparable retrieval performance. Furthermore, experiments on internal large-scale production systems yield substantial topline retrieval improvements along with high resilience to model staleness and superior data scaling. Our findings demonstrate that when augmented with modern representation learning, the traditional two-tower paradigm remains an exceptionally competitive and practical solution for industrial retrieval systems.
Zhe Xu, Prachi Agrawal, Kavosh Asadi et al.· 0 citations
Recommendation systems are indispensable for helping users navigate the vast product catalogs of modern online platforms. Recently, generative recommendation has emerged as a promising paradigm due to its unified and flexible formulation, and it has been adopted in various commercial scenarios. However, we find that different tokenization algorithms share a common issue: semantic branch drift, where semantically similar items are often assigned to different branches of the semantic tree. This phenomenon leads to severe information loss in the generated semantic IDs and significantly degrades recommendation accuracy. To address this problem, we propose HiST (Hierarchical Semantic Tree Augmentation), a novel framework that leverages branch information to bridge the semantic gap. The core innovation of HiST lies in using a trained tokenizer to construct a semantic tree over all items and extracting hierarchical information through a bottom-up approach. This hierarchical information is then fused with the original semantic IDs to enhance representation capacity. During end-to-end training, HiST improves recommendation performance by introducing separate prediction objectives for semantic IDs and hierarchical description components, enabling the model to effectively learn both discrete structure and semantic relationships. Experimental results demonstrate that HiST significantly improves recommendation performance across multiple datasets. Furthermore, experiments verify that HiST can be effectively integrated with various existing tokenization methods to enhance recommendation model performance, achieving consistent improvements across evaluated settings.
Bocheng Pan, Hailong Shi, Xingyu Gao· Proceedings of the 32nd ACM...· 0 citations
Generative Recommendation (GR) has emerged as a new paradigm for sequential recommendation, in which a representative line of work encodes items into hierarchical semantic IDs via residual quantization and predicts the IDs token by token. However, this generative formulation still exhibits structural gaps with respect to the recommendation task: flattening multi-token IDs into a single sequence destroys item-level structure, and the inconsistency between training and inference over a hierarchical codebook gives rise to semantic drift. To bridge these two gaps, we propose BARGE, which employs Item Context-Aware Attention (ICA) to restore item-level structure during encoding, and Hierarchical Path Reranking (HPR) together with Dual-Path Decoding (DPD) to suppress semantic drift from two complementary angles during decoding. Extensive experiments and analytical studies on public benchmarks and a large-scale offline test demonstrate that BARGE achieves superior recommendation performance. An online A/B test on a Tencent platform yields improvements of 0.60% in click-through rate, 1.34% in click unique visitors, and 1.70% in total reading time, confirming the practical value of BARGE in industrial-scale recommendation.
Sequential recommendation aims to predict users’ next items of interest based on their historical interactions. Recently, Large Language Models (LLMs) have shown strong potential in this field due to their powerful semantic understanding. However, existing methods face two core challenges: First, LLMs may generate non-existent recommendations due to hallucinations; Second, when utilizing LLM-generated embeddings for transfer to downstream recommendation tasks in different domains, the dimensional and structural differences across spaces exacerbate space misalignment. To address these challenges, we propose UniRec, a unified space learning framework that achieves deep alignment through a hierarchical space transformation path: language space → collaborative space → real space. This framework employs a two-stage learning mechanism: The first stage guides LLMs to align semantic and collaborative spaces through collaborative instruction fine-tuning, introduces a real-space constrained generation mechanism to reduce hallucination problems, and is optimized for large-scale applications; The second stage extracts core semantic principal components through singular value decomposition and freezes the representations, combines with gating networks to adaptively fuse semantic and domain-specific collaborative signals, achieving efficient cross-domain transfer. Experiments show that UniRec achieves over 27% average improvement on in-domain datasets and 24% on out-of-domain datasets, while maintaining low memory overhead.
Jiarui Yang, Junmei Feng, Yihan Zhang et al.· ACM Transactions on Informat...· 0 citations
