计算机应用研究2025，Vol.42Issue(1)：78-85,8.DOI:10.19734/j.issn.1001-3695.2024.05.0184
基于融合奖励的神经协同过滤去曝光偏差推荐模型
Neural collaborative filtering recommendation model for de-exposure bias based on fused rewards
摘要
Abstract
In recommendation systems,strong exposure bias caused by sparse interaction data and uneven exposure tends to concentrate recommendations on highly exposed items,neglecting the potential value of low-exposure items,thus limiting user choices and diminishing user experience.To address this issue,this paper proposed a model that integrated neural collabora-tive filtering and the linear upper confidence bound(LinUCB)algorithm to mitigate exposure bias.Firstly,the model used neural collaborative filtering to analyze interaction data between users and items,learning their features and capturing latent preferences.Secondly,it introduced the LinUCB algorithm,embedding its generated reward feature into the neural collabora-tive filtering model to enhance the exploration capabilities for low-exposure items.Finally,experiments conducted on the Mo-vieLens-100K and MovieLens-1M datasets demonstrated that this model increased exposure by approximately 60％compared to traditional neural collaborative filtering models.This enhancement suggests that the proposed method effectively mitigates expo-sure bias and improves both the accuracy and fairness of recommendations,thereby validating the effectiveness of the model.关键词
神经协同过滤/线性置信上界/曝光偏差/个性化推荐Key words
neural collaborative filtering/linear upper confidence bound/exposure bias/personalized recommendation分类
信息技术与安全科学引用本文复制引用
李鹏,李晓珊,朱心如..基于融合奖励的神经协同过滤去曝光偏差推荐模型[J].计算机应用研究,2025,42(1):78-85,8.基金项目
2023年哈尔滨商业大学青年科研创新人才培育计划资助项目(2023-KYYWF- 1001) （2023-KYYWF- 1001）
黑龙江省博士后科研启动金资助项目(BS0053) （BS0053）
