Understanding the real impact of multiple popularity biases in recommender systems
Authors:
- Shulue MeiSMShulue Mei
- Jie YuanJYJie Yuan
- Shuqing LiSLShuqing Li
- Yuxuan ZhuYZYuxuan Zhu
- Mingfeng JiangMJMingfeng Jiang
- 1 July 2026
- journal article
- Published by Elsevier in Big Data Research
- Vol. 45
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (62476121)
- National Social Science Fund of China (22BGL261)
This publication has 9 references indexed in Scilit:
- Toward Bias-Agnostic Recommender Systems: A Universal Generative FrameworkACM Transactions on Information Systems, 2024
- Popularity-aware sequential recommendation with user desireExpert Systems with Applications, 2023
- Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-setsInformation Sciences, 2022
- Dynamic Collaborative Filtering Based on User Preference Drift and Topic EvolutionIEEE Access, 2020
- Mitigating long tail effect in recommendations using few shot learning techniqueExpert Systems with Applications, 2020
- Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN FrameworkIEEE Access, 2019
- Popularity and Novelty Dynamics in Evolving NetworksScientific Reports, 2018
- What recommenders recommend: an analysis of recommendation biases and possible countermeasuresUser Modeling and User-Adapted Interaction, 2015
- Matrix Factorization Techniques for Recommender SystemsComputer, 2009