Abstract
Large recommender models have extended LLMs as powerful recommenders via encoding or item generation, and recent breakthroughs in LLM reasoning synchronously motivate the exploration of reasoning in recommendation. Current studies usually position LLMs as external reasoning modules to yield auxiliary thought for augmenting conventional recommendation pipelines. However, such decoupled designs are limited in significant resource cost and suboptimal joint optimization. To address these issues, we propose \name, a unified large recommender model with intrinsic reasoning capabilities. Initially, we reconceptualize the model architecture to facilitate interleaved reasoning and recommendation in the autoregressive process. Subsequently, we propose RecPO, a corresponding reinforcement learning framework that optimizes \name\ both the reasoning and recommendation capabilities simultaneously in a single policy update; RecPO introduces a fused reward scheme that solely leverages recommendation labels to simulate the reasoning capability, eliminating dependency on specialized reasoning annotations. Experiments on three datasets with various baselines verify the effectiveness of \name, showing relative improvements of 68.67\% in Hit@5 and 45.21\% in NDCG@20. Code available at this https URL.
Abstract (translated)
大型推荐模型已经通过编码或项目生成将大规模语言模型（LLM）扩展为强大的推荐系统，并且最近在LLM推理方面的突破同步激励了在推荐领域中探索推理能力。目前的研究通常将LLM定位为外部推理模块，以提供辅助思考来增强传统的推荐流水线。然而，这种解耦设计由于显著的资源成本和次优联合优化而受到限制。为了应对这些问题，我们提出了一个统一的大规模推荐模型\name，该模型具备内在的推理能力。 首先，我们重新构想模型架构，使推理与推荐在自回归过程中能够交错进行。随后，我们提出RecPO框架，这是一个对应的强化学习框架，它通过单一策略更新同时优化\name的推理和推荐能力；RecPO引入了一种融合奖励方案，仅利用推荐标签来模拟推理能力，消除了对专门推理注释的依赖。 在三个具有不同基线的数据集上进行的实验验证了\name的有效性，显示其Hit@5（点击率）相对提高了68.67%，NDCG@20（归一化折扣累积增益）相对提高了45.21%。代码可在提供的URL获取。 这一段文字描述了一个名为“\name”的新模型及其配套的强化学习框架RecPO的设计和效果，该系统旨在改进大型推荐模型通过内置推理能力来优化推荐性能的方法，并展示了其在实验中的优越表现。
URL
https://arxiv.org/abs/2505.16994
