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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8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
DIGER makes semantic IDs in generative recommendation differentiable via Gumbel noise and decay schedules, yielding consistent gains on public datasets by aligning indexing and recommendation losses.
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.
Co-evolving coder and tester models via consensus over a self-generated pass matrix improves LLM code generation up to 14.5% label-free and 21.6% with a lightly calibrated Bayesian selector.
BLOGER is a bi-level optimization framework that jointly optimizes the tokenizer and recommender for generative recommendation, outperforming prior methods on real-world datasets.
FedMM applies a residual quantized VAE with a global federated codebook and local market-specific codebooks to transmit discrete codes that capture shared and specific collaborative patterns for improved CTR prediction across markets.
LLM agents enable a shift in recommender systems from opaque hidden profiles to governable, inspectable, and portable user representations.
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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Differentiable Semantic ID for Generative Recommendation
DIGER makes semantic IDs in generative recommendation differentiable via Gumbel noise and decay schedules, yielding consistent gains on public datasets by aligning indexing and recommendation losses.
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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
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RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.
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ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
Co-evolving coder and tester models via consensus over a self-generated pass matrix improves LLM code generation up to 14.5% label-free and 21.6% with a lightly calibrated Bayesian selector.
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Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation
BLOGER is a bi-level optimization framework that jointly optimizes the tokenizer and recommender for generative recommendation, outperforming prior methods on real-world datasets.
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FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
FedMM applies a residual quantized VAE with a global federated codebook and local market-specific codebooks to transmit discrete codes that capture shared and specific collaborative patterns for improved CTR prediction across markets.
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From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents
LLM agents enable a shift in recommender systems from opaque hidden profiles to governable, inspectable, and portable user representations.