Grevo is 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.
Abstract
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.
BARGE is proposed, 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.
The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language generation share a similarity: both aim to produce an ordered sequence that optimizes the user's experience. However, how to precisely absorb the essence of the LLM paradigm into mature industrial recommender systems remains an open problem. There are two challenges. First, it is unclear how to incorporate sequence-level generation and optimization from the LLM paradigm into recommendation. Second, real-world recommender systems are mature systems that have been iteratively customized for years around specific products, business constraints, serving infrastructure, and organizational ownership. Replacing such systems wholesale is often technically risky and organizationally disruptive. In this paper, we propose LIGE-GR, a listwise generation and evaluation recommendation framework that upgrades from a traditional ranking system based on itemwise recommendation toward a generative recommendation paradigm. Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system. This allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and serving infrastructure. We validate LIGE-GR in short-video recommendation on Instagram Reels and Facebook Video. On these recommendation surfaces, LIGE-GR improves time spent by 1.14 percent on Instagram Reels and 0.72 percent on Facebook Video, while requiring only modest additional inference resources.
Venkat Srinivas, Chen-Zhang He, Sam Woodmansee et al.· 0 citations
Generative recommendation (GR) assigns each item content-derived Semantic IDs (SIDs) and employs a sequence-to-sequence model to generate the target item's SIDs based on past user sessions. This paradigm typically outperforms sequential recommendation (SR) methods that rely solely on behavior modeling. However, GR methods suffer from semantic preference bias, where they favor semantically similar items while neglecting items with weak semantic similarity but manifest behavioral relevance--patterns that SR models capture more effectively through behavior modeling. In this work, we propose OMG, a GR method that unifies behavior modeling and semantic generation to produce recommendations that seamlessly integrate both information. First, we introduce Hybrid Behavior-Semantic Encoding, enabling OMG to capture salient user behavioral patterns in addition to semantic correlation when encoding interaction sequences, aiming to recall items with certain behavioral relevance but weak semantic similarity. Then, we propose Behavior-Semantic Latent Reasoning. It iteratively refines the encoder output, progressively transforming it from the behavioral space to the semantic space, and leverages the resulting trajectory to guide SIDs decoding, thereby bridging behavior modeling and semantic generation. Finally, we employ Behavior-Reinforced Semantic Generation, which introduces reinforcement learning with behavior-aware rewards to enhance the model's reasoning capability and better align the decoder's generation process with user behavior. Experiments on benchmark datasets demonstrate that OMG consistently outperforms baseline methods.
Binquan Wu, Xinbo Chen, Yicheng Luo et al.· Proceedings of the 32nd ACM...· 0 citations
Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily focus on modeling individual user sequences within a constrained internal parametric space, failing to explicitly leverage cross-user collaborative signals. To address this issue, we propose OMEGA, a cOllaborative MEmory augmentation framework for Generative recommendAtion. OMEGA bridges the gap between implicit parametric knowledge and explicit collaborative signals. We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations, significantly reducing storage overhead. These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns. To ensure precise knowledge acquisition, we design a lightweight and target-aware retrieval mechanism that identifies pertinent memories by considering both sequence-level and target-level similarities. Furthermore, a context-aware integration module, equipped with a gated cross-attention mechanism, is employed to adaptively fuse the retrieved collaborative memories with the local user context while mitigating the interference of noisy patterns. Empirical evaluations on multiple real-world datasets demonstrate that OMEGA significantly outperforms existing advanced GR models, validating the potential of external memory as a complement to the generative paradigm.
Enze Liu, Zhen Tian, W. Zhao· Proceedings of the 32nd ACM...· 0 citations
A Hierarchical Semantic Alignment module to align query's latent space with item's quantization path and synchronize multi-granular semantics, and a personalized GR framework that models user behavior by synergizing discrete SIDs for structural guidance and continuous representations for fine-grained semantic refinement are constructed.
Gao-Ming Zhang, Ang-Qing Jiang, Jian-Chun Song et al.· 0 citations
Hierarchical Residual Policy Optimization (HRPO), a post-training framework that converts item-level outcomes into dense, token-aligned learning signals for conservative token-wise improvement, is proposed.
Kaifeng Guo, Yiming Yang, Jingtong Gao et al.· Proceedings of the 32nd ACM...· 0 citations
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