A survey on causal inference for recommendation

作     者：Huishi Luo Fuzhen Zhuang Ruobing Xie Hengshu Zhu Deqing Wang Zhulin An Yongjun Xu 

作者机构：Institute of Artificial IntelligenceBeihang UniversityBeijing 100191China Zhongguancun LaboratoryBeijing 100094China WeChat Search Application DepartmentTencentBeijing 100080China The Career Science LaboratoryBOSS ZhipinBeijing 100028China SKLSDESchool of Computer ScienceBeihang UniversityBeijing 100191China Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China 

出 版 物：《The Innovation》 (创新（英文）)

年 卷 期：2024年第5卷第2期

页      面：130-144页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This review is supported by the National Key Research and Development Program of China under grant no.2021ZD0113602 the National Natural Science Foundation of China under grant nos.62176014 and 62276015 the Fundamental Research Funds for the Central Universities 

主　　题：survey details causal 

摘      要：Causal inference has recently garnered significant interest among recommender system(RS)researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple *** offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data *** there are already some valuable surveys on causal recommendations,they typically classify approaches based on the practical issues faced in RS,a classification that may disperse and fragment the uni-fied causal *** RS researchers’unfamiliarity with causality,it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective,thereby facilitating a deeper integration of causal inference in *** survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal ***,we introduce the fundamental concepts of causal inference as the basis of the following ***,we propose a novel theory-driven taxonomy,categorizing existing methods based on the causal theory employed,namely those based on the potential outcome framework,the structural causal model,and general *** review then delves into the technical details of how existing methods apply causal inference to address particular recommender ***,we highlight some promising directions for future research in this *** papers and open-source resources will be progressively available at https://***/Chrissie-Law/Causal-Inference-forRecommendation.