RecRM-Bench is a new large-scale benchmark dataset and framework for multi-dimensional reward modeling in agentic recommender systems, spanning instruction following, factual consistency, query-item relevance, and user behavior prediction.
Rec-r1: Bridging generative large language mod- els and user-centric recommendation systems via reinforcement learning
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7verdicts
UNVERDICTED 7representative citing papers
TF-LLMER resolves optimization barriers in LLM-enhanced recommenders through embedding normalization and Rec-PCA that aligns semantic representations with collaborative co-occurrence graphs.
DUET uses a three-stage joint profile generator with RL feedback to create consistent user-item textual profiles that outperform independent generation in recommendation tasks.
S²GR adds stepwise thinking tokens with contrastive supervision on codebook clusters to balance computational focus and ground reasoning paths in generative recommendation.
ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
ABPO combines group-relative policy optimization with anchored exposure correction and asymmetric feedback handling to enable effective continual updates for LLM recommenders under bandit feedback constraints.
citing papers explorer
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RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
RecRM-Bench is a new large-scale benchmark dataset and framework for multi-dimensional reward modeling in agentic recommender systems, spanning instruction following, factual consistency, query-item relevance, and user behavior prediction.
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Break the Optimization Barrier of LLM-Enhanced Recommenders: A Theoretical Analysis and Practical Framework
TF-LLMER resolves optimization barriers in LLM-enhanced recommenders through embedding normalization and Rec-PCA that aligns semantic representations with collaborative co-occurrence graphs.
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DUET: Joint Exploration of User Item Profiles in Recommendation System
DUET uses a three-stage joint profile generator with RL feedback to create consistent user-item textual profiles that outperform independent generation in recommendation tasks.
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S$^2$GR: Stepwise Semantic-Guided Reasoning in Latent Space for Generative Recommendation
S²GR adds stepwise thinking tokens with contrastive supervision on codebook clusters to balance computational focus and ground reasoning paths in generative recommendation.
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Conditional Memory Enhanced Item Representation for Generative Recommendation
ComeIR introduces dual-level Engram memory and memory-restoring prediction to reconstruct SID-token embeddings and restore token granularity in generative recommendation.
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RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
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Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
ABPO combines group-relative policy optimization with anchored exposure correction and asymmetric feedback handling to enable effective continual updates for LLM recommenders under bandit feedback constraints.