Recommender System
Recommender systems aim to predict user preferences and provide personalized recommendations, enhancing user experience across various online platforms. Current research emphasizes improving accuracy and mitigating biases, focusing on advanced techniques like neural networks (including transformers and recurrent networks), matrix factorization, and ensemble methods to address challenges such as data sparsity, outlier detection, and the impact of algorithmic bias on user preferences. This field is significant due to its widespread applications and the growing need for responsible and ethical design, driving research into explainability, fairness, and the use of causal inference to understand and mitigate the societal impact of these systems.
Papers
Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu, Shuo Su, Kaiqiao Zhan, Ben Wang
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
Thanh Toan Nguyen, Quoc Viet Hung Nguyen, Thanh Tam Nguyen, Thanh Trung Huynh, Thanh Thi Nguyen, Matthias Weidlich, Hongzhi Yin
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu
Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System
Sein Kim, Hongseok Kang, Seungyoon Choi, Donghyun Kim, Minchul Yang, Chanyoung Park
How Does Message Passing Improve Collaborative Filtering?
Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao
A Novel Behavior-Based Recommendation System for E-commerce
Reza Barzegar Nozari, Mahdi Divsalar, Sepehr Akbarzadeh Abkenar, Mohammadreza Fadavi Amiri, Ali Divsalar
A Recommender System for NFT Collectibles with Item Feature
Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee, Joohwan Hong, Yongjae Lee