Poster "recommender systems" Papers
13 papers found
Conference
Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Qijiong Liu, Jieming Zhu, Lu Fan et al.
NEURIPS 2025arXiv:2503.05493
4
citations
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang, Shuchang Liu et al.
NEURIPS 2025arXiv:2509.26063
Language Representations Can be What Recommenders Need: Findings and Potentials
Leheng Sheng, An Zhang, Yi Zhang et al.
ICLR 2025arXiv:2407.05441
26
citations
MultiScale Contextual Bandits for Long Term Objectives
Richa Rastogi, Yuta Saito, Thorsten Joachims
NEURIPS 2025arXiv:2503.17674
Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
Leqi Zheng, Chaokun Wang, Zixin Song et al.
NEURIPS 2025
Normed Spaces for Graph Embedding
Wei Zhao, Diaaeldin Taha, J. Riestenberg et al.
ICLR 2025arXiv:2312.01502
1
citations
ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Jingyuan He, Jiongnan Liu, Vishan Oberoi et al.
NEURIPS 2025arXiv:2510.26095
PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
Ruixin Guo, Ruoming Jin, Xinyu Li et al.
NEURIPS 2025arXiv:2512.12905
1
citations
TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Jiahao Yu, Haozhuang Liu, Yeqiu Yang et al.
NEURIPS 2025arXiv:2505.13881
2
citations
True Impact of Cascade Length in Contextual Cascading Bandits
Hyun-jun Choi, Joongkyu Lee, Min-hwan Oh
NEURIPS 2025
High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion
Yu Dai, Junchen Shen, Zijie Zhai et al.
ICML 2024
MoMo: Momentum Models for Adaptive Learning Rates
Fabian Schaipp, Ruben Ohana, Michael Eickenberg et al.
ICML 2024arXiv:2305.07583
20
citations
On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
Álvaro Labarca Silva, Denis Parra, Rodrigo A Toro Icarte
ICML 2024