Recommendation Problem

The recommendation problem focuses on algorithmically providing personalized suggestions to users based on their preferences and past behavior, aiming to alleviate information overload and enhance user experience. Current research emphasizes addressing challenges like hyperparameter optimization for improved model performance, mitigating biases and cold-start issues through techniques such as causal inference and matrix factorization variations, and incorporating contextual information (e.g., user sessions, social networks) into more sophisticated models like graph-based and sequence-aware approaches. These advancements have significant implications for various applications, including e-commerce, media streaming, and online education, by improving the accuracy, fairness, and robustness of recommendation systems.

Papers

November 3, 2024

Efficient and Robust Regularized Federated Recommendation
Langming Liu, Wanyu Wang, Xiangyu Zhao, Zijian Zhang, Chunxu Zhang, Shanru Lin, Yiqi Wang, Lixin Zou, Zitao Liu, Xuetao Wei, Hongzhi Yin, Qing Li
High Efficiency Federated Learning Recommender System Centralized Learning Federated Recommendation Recommendation Problem

September 16, 2024

Causal Discovery in Recommender Systems: Example and Discussion
Emanuele Cavenaghi, Fabio Stella, Markus Zanker
Recommender System Causal Discovery Causal Graph Causal Pattern Single Example Discussion Paper Recommendation Problem Causal Discovery Task

August 14, 2024

Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
Hui Fang, Xu Feng, Lu Qin, Zhu Sun
Recommender System Hyperparameter Optimization Recommendation Model Comprehensive Evaluation Implicit Feedback Top N Recommendation Recommendation Problem Recommendation Benchmark

May 18, 2024

Graph Feedback Bandits with Similar Arms
Han Qi, Guo Fei, Li Zhu
Multi Armed Bandit UCB Algorithm Feedback Graph Recommendation Problem Free Regret Graph Bandit Many Arm

August 12, 2023

Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
Jiayi Liu, Jennifer Neville
Multi Objective Textual Feature Personalized Ranking Dynamic Balance Direct Preference Ranking Method Recommendation Problem

February 6, 2023

Recommender Systems: A Primer
Pablo Castells, Dietmar Jannach
Recommender System Personalized Recommendation Session Based Recommendation Recommendation Problem

December 6, 2022

PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Hao Wang
Recommender System Probabilistic Model Matrix Factorization Poisson Noise Recommendation Problem Cold Start Problem

November 1, 2022

Reinforcement Learning in Education: A Multi-Armed Bandit Approach
Herkulaas Combrink, Vukosi Marivate, Benjamin Rosman
Reinforcement Learning Online Learning Education Domain Agent Based Model Reinforcement Learning Research State Reward Recommendation Problem

September 30, 2022

February 8, 2022

CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang
Recommender System Causal Learning DAG Learning Recommendation Problem

January 13, 2022

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
Ruichu Cai, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu
Recommendation Algorithm Debiased Recommendation Social Recommendation Rest RESTAD NAP Exposure Bias Recommendation Problem

December 2, 2021

Recommending with Recommendations
Naveen Durvasula, Franklyn Wang, Scott Duke Kominers
Latent Space Recommendation System Movie Recommendation Recommendation Problem