"recommender systems" Papers
24 papers found
Conference
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li et al.
Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Qijiong Liu, Jieming Zhu, Lu Fan et al.
Direct Routing Gradient (DRGrad): A Personalized Information Surgery for Multi-Task Learning (MTL) Recommendations
Yuguang Liu, Yiyun Miao, Luyao Xia
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang, Shuchang Liu et al.
Language Representations Can be What Recommenders Need: Findings and Potentials
Leheng Sheng, An Zhang, Yi Zhang et al.
MultiScale Contextual Bandits for Long Term Objectives
Richa Rastogi, Yuta Saito, Thorsten Joachims
Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
Leqi Zheng, Chaokun Wang, Zixin Song et al.
Normed Spaces for Graph Embedding
Wei Zhao, Diaaeldin Taha, J. Riestenberg et al.
One for Dozens: Adaptive REcommendation for All Domains with Counterfactual Augmentation
Huishi Luo, Yiwen Chen, Yiqing Wu et al.
ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Jingyuan He, Jiongnan Liu, Vishan Oberoi et al.
PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
Ruixin Guo, Ruoming Jin, Xinyu Li et al.
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Zhongjian Zhang, Mengmei Zhang, Xiao Wang et al.
TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems
Jiahao Yu, Haozhuang Liu, Yeqiu Yang et al.
True Impact of Cascade Length in Contextual Cascading Bandits
Hyun-jun Choi, Joongkyu Lee, Min-hwan Oh
Who You Are Matters: Bridging Interests and Social Roles via LLM-Enhanced Logic Recommendation
Qing Yu, Xiaobei Wang, Shuchang Liu et al.
Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
Lei Li, Jianxun Lian, Xiao Zhou et al.
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Ximei Wang, Junwei Pan, Xingzhuo Guo et al.
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Aritra Bhowmick, Mert Kosan, Zexi Huang et al.
High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion
Yu Dai, Junchen Shen, Zijie Zhai et al.
MoMo: Momentum Models for Adaptive Learning Rates
Fabian Schaipp, Ruben Ohana, Michael Eickenberg et al.
On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
Álvaro Labarca Silva, Denis Parra, Rodrigo A Toro Icarte
Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering
Haoxuan Li, Chunyuan Zheng, Shuyi Wang et al.
STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang et al.
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Xinyu Lin, Wenjie Wang, Jujia Zhao et al.