Proposes IPSsim, integrating content similarity to improve bias correction, boosting NDCG by ~3%.

Zeyan Liang, Graham McDonald, Iadh Ounis

PruneRec combines three-stage structured pruning and distillation, retaining 88% performance with over 95% of non-embedding parameters removed.

Shanle Zheng, Keqin Bao, Jizhi Zhang et al.

ARAG employs multi-agent reasoning—user understanding, semantic inference, context summarization, and ranking—achieving up to 42.1% NDCG@5 improvement in personalized recommendations.

Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta et al.

Analyzes two-tower model identifiability and bias amplification; proposes sample weighting to mitigate bias effects.

Philipp Hager, Onno Zoeter, Maarten de Rijke

Proposes a relation-based taxonomy for click models, introducing three key design choices to unify PGMs and NNs across interfaces.

Jingwei Kang, Maarten de Rijke, Santiago de Leon-Martinez et al.

GFlowGR, a GFlowNet-based fine-tuning framework, effectively mitigates exposure bias in generative recommendation.

Yejing Wang, Shengyu Zhou, Jinyu Lu et al.

OneRec employs an end-to-end generative architecture, boosting recommendation model FLOPs tenfold, significantly improving computational efficiency and resource utilization, deployed at Kuaishou, enhancing user engagement.

Guorui Zhou, Jiaxin Deng, Jinghao Zhang et al.

GainRAG aligns retriever and LLM preferences via the 'gain' metric, boosting performance across six datasets.

Yi Jiang, Sendong Zhao, Jianbo Li et al.

DeepRec enhances recommendation by multi-turn interactions between LLMs and TRMs, significantly improving performance.

Bowen Zheng, Xiaolei Wang, Enze Liu et al.

Agent4SR uses LLMs for low-knowledge, high-impact attacks, enhancing manipulation of recommender systems.

Shengkang Gu, Jiahao Liu, Dongsheng Li et al.

DARLR optimizes recommender systems with dynamic rewards, showing a 16% improvement on KuaiRand.

Yi Zhang, Ruihong Qiu, Xuwei Xu et al.

This study systematically analyzes how pre-training and fine-tuning influence knowledge acquisition in dense retrieval models, confirming that fine-tuning mainly adjusts neuron activation rather than reorganizing stored knowledge.

Zheng Yao, Shuai Wang, Guido Zuccon

AutoNuggetizer automatically extracts atomic facts from LLM responses, correlating strongly with human preferences (p<1e-24).

Sahel Sharifymoghaddam, Shivani Upadhyay, Nandan Thakur et al.

A large language model-based automatic nugget extraction and assignment framework for RAG evaluation, validated against human annotations with high correlation.

Ronak Pradeep, Nandan Thakur, Shivani Upadhyay et al.

Proposes LARM framework with multimodal LLM fine-tuning, embedding alignment, and semantic coding to improve live-streaming recommendation.

Yueyang Liu, Jiangxia Cao, Shen Wang et al.

CORAL employs contrastive preference expansion and learning, achieving up to 99.72% Recall@10, significantly improving retrieval-based conversational recommendation.

Heejin Kook, Junyoung Kim, Seongmin Park et al.

LLMSeR enhances sequential recommendation by generating pseudo-prior items using LLMs.

Yuqi Sun, Qidong Liu, Haiping Zhu et al.

LLM Agents enhance recommendation and search systems, significantly improving information retrieval efficiency.

Yu Zhang, Shutong Qiao, Jiaqi Zhang et al.

OneRec employs an end-to-end generative model with session-wise decoding and preference alignment, achieving 1.6% watch-time increase in industry deployment.

Jiaxin Deng, Shiyao Wang, Kuo Cai et al.

This paper reviews the evolution of IR architectures, emphasizing transformer-based models and large language models, with performance metrics like nDCG@10 reaching 0.45 on MS MARCO.

Zhichao Xu, Fengran Mo, Zhiqi Huang et al.