Exploring Exposure Bias in Recommender Systems from Causality Perspective
文献类型:会议论文
作者 | Yang, Yi1; Li, Meng1; Hu, Xueyang2; Pan, Guoyang1,3; Huang, Weixing1,4; Wang, Jian1; Wang,Yun1 |
出版日期 | 2021 |
会议日期 | 2021-12-06 |
会议地点 | Hainan Island, China |
关键词 | exposure bias causal inference implicit feedback survey causality recommender system |
英文摘要 | Exposure bias widely exists in recommender systems, particularly in the case of with implicit feedbacks. It seriously influences user's satisfaction of recommendations. There are a number of methods for mitigating the exposure bias from different perspectives. In this paper, we survey the publications that focus on addressing the exposure bias issue in RS with the help of causal inference ideas. We propose a simple taxonomy consisting of bias discovery, evaluation estimator, recommendation modeling, ranking algorithm for the debiasing methods in our study. Based on the taxonomy, we discuss how those methods are beneficial to recommender systems to mitigate the exposure bias using causal graph and propensity score. Finally, we conduct the challenges and point out the future research directions. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/47408] |
专题 | 数字内容技术与服务研究中心_智能技术与系统工程 |
通讯作者 | Wang, Jian |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Maryland 3.School of Artificial Intelligence, University of Chinese Academy of Sciences 4.CASIA-Junsheng (Shenzhen) Intelligent & Big Data Sci-Tech Development Ltd. |
推荐引用方式 GB/T 7714 | Yang, Yi,Li, Meng,Hu, Xueyang,et al. Exploring Exposure Bias in Recommender Systems from Causality Perspective[C]. 见:. Hainan Island, China. 2021-12-06. |
入库方式: OAI收割
来源:自动化研究所
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