R^2ec: Towards Large Recommender Models with Reasoning
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
https://github.com/YRYangang/RRec.
683fb5d3a09aefea70733fa3
Runyang You
extracted_confirmed
2025-06-04T07:34:53.226Z
64e14c5b12a5504dda70e60d
/avatars/944b486bb037364ef7d9d2c826526708.svg
Runyang
user
dd101bb
683fb5d3a09aefea70733fa4
Yongqi Li
extracted_pending
2025-06-04T02:56:21.008Z
674038313ccfb67446ae2b35
/avatars/8a3c0fdf971363988731f9eb8b13658c.svg
tensorslow
user
tensorslow
683fb5d3a09aefea70733fa5
Xinyu Lin
683fb5d3a09aefea70733fa6
Xin Zhang
claimed_verified
2025-06-04T08:55:59.107Z
63b6dbc8ccebeadccc888456
https://cdn-avatars.huggingface.co/v1/production/uploads/1673396893898-63b6dbc8ccebeadccc888456.jpeg
Xin Zhang
user
izhx
683fb5d3a09aefea70733fa7
Wenjie Wang
683fb5d3a09aefea70733fa8
Wenjie Li
683fb5d3a09aefea70733fa9
Liqiang Nie
R^2ec: Towards Large Recommender Models with Reasoning
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
https://github.com/YRYangang/RRec.
A unified large recommender model with intrinsic reasoning capabilities is proposed, facilitating interleaved reasoning and recommendation using a reinforcement learning framework called RecPO.
recommender models LLMs intrinsic reasoning autoregressive process reinforcement learning RecPO fused reward scheme Hit@5 NDCG@20