Effective Recommendation

Effective recommendation aims to predict user preferences and provide personalized suggestions, focusing on improving accuracy, efficiency, and fairness. Current research emphasizes incorporating diverse data sources (e.g., multimodal information, user reviews, knowledge graphs) and advanced model architectures (e.g., graph neural networks, large language models, and various contrastive learning methods) to address challenges like cold-start problems and noisy user data. These advancements are significant for enhancing user experience in various applications (e.g., e-commerce, entertainment, job recruitment) and for developing more robust and explainable recommendation systems.

Papers

June 7, 2022

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu
Effective Recommendation Partial Ranking Deep Learning Application Device Training Device Learning Federated Ensemble Knowledge Capacity

May 26, 2022

Fairness in Recommendation: Foundations, Methods and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang
Financial Application Recommender System Procedural Fairness NCD Method Effective Recommendation Flawed Foundation Fairness Research Recommendation Fairness

May 23, 2022

May 10, 2022

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
Haoxuan Li, Chunyuan Zheng, Peng Wu
Raw Data Recommender System Effective Recommendation Robust Learning Imputation Algorithm Random Participation Double Robustness

May 2, 2022

Knowledge Graph Contrastive Learning for Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li
Knowledge Graph Effective Recommendation Graph Based Knowledge Graph Contrastive

April 29, 2022

Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu
Effective Recommendation Exposure Fairness

April 26, 2022

Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approaches
Hichem Ammar Khodja, Oussama Boudjeniba
Generative Adversarial Network Application Proficiency Constructive Approach Effective Recommendation Relative Relevance Wasserstein Gan

April 23, 2022

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao
Effective Recommendation Session Based Recommendation Item Embeddings Input Embeddings Complex ECC Core Consistent Representation Consistent Representation Learning

April 21, 2022

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie
Contrastive Learning Privacy Preserving Effective Recommendation Recommendation Model Local Differential Privacy Federated Contrastive Learning

April 8, 2022

IA-GCN: Interactive Graph Convolutional Network for Recommendation
Yinan Zhang, Pei Wang, Congcong Liu, Xiwei Zhao, Hao Qi, Jie He, Junsheng Jin, Changping Peng, Zhangang Lin, Jingping Shao
Effective Recommendation Graph Convolutional Network Collaborative Filtering User Item

April 5, 2022

A Survey on Dropout Methods and Experimental Verification in Recommendation
Yangkun Li, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma, Yuekui Yang
Timely Survey Effective Recommendation Structured Dropout Experimental Verification Dropout Method Invariant Dropout

April 3, 2022

April 2, 2022

Bayesian Negative Sampling for Recommendation
Bin Liu, Bang Wang
Effective Recommendation Negative Sampling

March 30, 2022

March 29, 2022

Requirements Elicitation in Cognitive Service for Recommendation
Bolin Zhang, Zhiying Tu, Yunzhe Xu, Dianhui Chu, Xiaofei Xu
Effective Recommendation Response Generation Response Generation Model Requirement Elicitation Azure Cognitive Search

March 24, 2022

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang
Natural Language Processing Natural Language Multi Task Effective Recommendation Sequential Recommendation Unified Pre Training

Effective Recommendation

Papers

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning

Fairness in Recommendation: Foundations, Methods and Applications

Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints

KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation

Fairness in Recommender Systems: Research Landscape and Future Directions

StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random

Knowledge Graph Contrastive Learning for Recommendation

Joint Multisided Exposure Fairness for Recommendation

Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approaches

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

IA-GCN: Interactive Graph Convolutional Network for Recommendation

A Survey on Dropout Methods and Experimental Verification in Recommendation

VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation

A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations

Bayesian Negative Sampling for Recommendation

Recommendation of Compatible Outfits Conditioned on Style

Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation

Requirements Elicitation in Cognitive Service for Recommendation

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)