计算机技术与发展2024，Vol.34Issue(9)：102-108,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0176
一种基于因果推断的序列推荐模型
A Sequence Recommendation Model Based on Causal Inference
摘要
Abstract
In response to the performance degradation issue in recommendation models caused by the inconsistency between training and testing data distributions,i.e.,out-of-distribution(OOD)scenarios,we propose a novel optimized sequential recommendation model called CCSRec(Counterfactual Context Sequential Recommendation)based on causal inference.By leveraging backdoor adjustment and counterfactual methods,CCSRec effectively removes spurious correlations between users and items,thus reducing the adverse influence of OOD on recommendation performance.CCSRec generates counterfactual environments by replacing scenario data to enhance the model's learning ability for contextual data,effectively mitigating the performance degradation problem under OOD scenarios.Additionally,CCSRec incorporates user information in its parameterization to further enhance recommendation effectiveness.Experimental results on multiple publicly recommended datasets indicate that the CCSRec model further reduces the adverse effects of out-of-distribution situations on performance by use of scenario data replacement for counterfactual sequence generation,and performance is significantly im-proved.关键词
推荐系统/序列推荐/因果推断/分布外泛化/深度学习Key words
recommender system/sequential recommendation/causal inference/out-of-distribution generalization/deep learning分类
信息技术与安全科学引用本文复制引用
朱明朔,沈苏彬..一种基于因果推断的序列推荐模型[J].计算机技术与发展,2024,34(9):102-108,7.基金项目
国家自然科学基金(62002174) （62002174）
