EAGER-LLM integrates exogenous behavior and semantic signals via knowledge-rich indices and multi-scale alignment, boosting large language model recommendation performance.

Minjie Hong, Yan Xia, Zehan Wang et al.

Proposes DualIPW, combining query-level and position-level click propensity estimation, to mitigate relevance saturation bias in unbiased learning to rank.

Lulu Yu, Keping Bi, Jiafeng Guo et al.

LLM-powered agents enhance recommender systems through natural language interaction and explainability.

Qiyao Peng, Hongtao Liu, Hua Huang et al.

CreAgent integrates game theory belief models and LLMs to simulate creator strategies, improving long-term recommender system evaluation.

Xiaopeng Ye, Chen Xu, Zhongxiang Sun et al.

LiveForesighter uses Transformers to forecast live-stream highlights and future products; it was deployed across Kuaishou services serving 400 million daily active users.

Yucheng Lu, Jiangxia Cao, Xu Kuan et al.

MMDocIR benchmark introduces dual-level multimodal long document retrieval, outperforming text-only models with significant improvements in Recall@1 and layout understanding.

Kuicai Dong, Yujing Chang, Xin Deik Goh et al.

Proposed interval-based tokenization for symbolic music, improving model performance and interpretability.

Dinh-Viet-Toan Le, Louis Bigo, Mikaela Keller

SMORE model reduces noise through frequency domain fusion, enhancing multimodal recommendation accuracy.

Rongqing Kenneth Ong, Andy W. H. Khong

Survey on recommendation unlearning, addressing method and challenges to erase specific training data memory.

Yuyuan Li, Xiaohua Feng, Chaochao Chen et al.

Proposes 2D Matryoshka training, combining multi-layer and multi-dimension embeddings, significantly improving STS and retrieval performance.

Shuai Wang, Shengyao Zhuang, Bevan Koopman et al.

Proposes offline R/R* metric and scaling law for online ad retrieval models, enabling cost-effective performance prediction.

Yunli Wang, Zhen Zhang, Zixuan Yang et al.

Proposed Geo-Time Re-ranking (GT-R) model using LLMs, achieving 15%-20% improvement on LEO dataset for event recommendation.

Yuanyuan Tian, Wenwen Li, Lei Hu et al.

Proposes SCaLR, a self-calibrated listwise reranking framework using explicit relevance scores, improving efficiency and global comparability in large candidate sets.

Ruiyang Ren, Yuhao Wang, Kun Zhou et al.

VisRAG employs vision-language models for end-to-end multi-modal document retrieval and generation, achieving 20-40% performance gains over traditional text-based RAG.

Shi Yu, Chaoyue Tang, Bokai Xu et al.

Proposes a domain-adapted reward model for offline ad ranking evaluation, outperforming IPS and non-generalized models.

Mohamed A. Radwan, Himaghna Bhattacharjee, Quinn Lanners et al.

Vulnerability-aware Adversarial Training (VAT) adaptively adjusts perturbation magnitudes based on user fit, significantly improving robustness against poisoning attacks.

Kaike Zhang, Qi Cao, Yunfan Wu et al.

This study evaluates the impact of logging policy bias on two-tower ULTR models using Baidu dataset, finding bias conditions met but limited effect on performance.

Morris de Haan, Philipp Hager

Promptriever is the first instruction-trained dense retriever, achieving state-of-the-art results with nearly 500k instruction-augmented pairs.

Orion Weller, Benjamin Van Durme, Dawn Lawrie et al.

TRC Bench evaluates 19 LLMs and organizes RAG trustworthiness into six dimensions.

Yujia Zhou, Wenbo Zhang, Jingying Shao et al.

Proposes multi-category negative sampling strategies, enhancing detection of hard negatives in recommendation models.

Haokai Ma, Ruobing Xie, Lei Meng et al.