提升生成式推荐模型性能，让其媲美传统方法
Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling
- 用协同信息优化物品令牌表示，增强推荐相关性
- 新模型在多个基准上达到与ID基线相当的准确率
- 适合想用生成范式但追求高效率的推荐系统研究者
近期工作将生成式推荐系统作为传统基于ID的模型替代方案，将物品推荐重构为离散物品令牌序列生成任务。尽管前景可观，这类方法在实践中仍常落后于经调优的ID基线模型（如SASRec）。本文识别出两大限制因素：物品令牌表示中缺乏协同信号，以及常用编码器-解码器架构的效率问题。为此，提出COSETTE，一种对比式令牌化方法，将协同信息直接融入物品表征，联合优化内容重建与推荐相关性。同时提出MARIUS，一种轻量级、受音频启发的生成模型，分离时间序列建模与物品解码过程。该模型降低推理开销并提升推荐精度。在标准序列推荐基准上的实验表明，所提方法缩小甚至消除生成式与现代ID基线之间的性能差距，同时保留生成范式的优点。
原文摘要 · Abstract (English)
Recent work has explored generative recommender systems as an alternative to traditional ID-based models, reframing item recommendation as a sequence generation task over discrete item tokens. While promising, such methods often underperform in practice compared to well-tuned ID-based baselines like SASRec. In this paper, we identify two key limitations holding back generative approaches: the lack of collaborative signal in item tokenization, and inefficiencies in the commonly used encoder-decoder architecture. To address these issues, we introduce COSETTE, a contrastive tokenization method that integrates collaborative information directly into the learned item representations, jointly optimizing for both content reconstruction and recommendation relevance. Additionally, we propose MARIUS, a lightweight, audio-inspired generative model that decouples timeline modeling from item decoding. MARIUS reduces inference cost while improving recommendation accuracy. Experiments on standard sequential recommendation benchmarks show that our approach narrows, or even eliminates, the performance gap between generative and modern ID-based models, while retaining the benefits of the generative paradigm.
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