电子科技大学学报2024，Vol.53Issue(3)：396-403,8.DOI:10.12178/1001-0548.2023116
基于生成对抗网络的评分可信推荐模型
Rating-Trustworthy Recommendation Model Based on Generative Adversarial Networks
摘要
Abstract
Existing deep learning-based recommendation models have mainly focused on improving the accuracy of recommendation systems.However,beyond recommendation accuracy,the reliability of the model's recommendations is also of great concern.Therefore,a rating-trustworthy recommendation model based on generative adversarial networks(GANs)is proposed to evaluate the effectiveness of prediction results and achieve a balance between recommendation accuracy and reliability.This model solely employs explicit user rating information to gauge the credibility of predicted ratings and screens out highly credible predicted ratings based on a predefined reliability threshold,thus ensuring the trustworthiness of recommended items.Furthermore,to enhance the prediction performance of the model and ensure fairness in training,a positive sample padding strategy is designed to mitigate the data imbalance problem in the rating reliability matrix.Experimental results on three real datasets show that the proposed model outperforms selected comparison methods in both Recall and NDCG metrics,effectively improving the performance of recommendation systems.关键词
生成对抗网络/填充策略/可靠性/推荐系统Key words
generative adversarial networks/filling strategy/reliability/recommender systems分类
信息技术与安全科学引用本文复制引用
王永,王淞立,邓江洲..基于生成对抗网络的评分可信推荐模型[J].电子科技大学学报,2024,53(3):396-403,8.基金项目
国家自然科学基金(62272077,72301050) （62272077,72301050）
重庆市自然科学面上基金(cstc2021jcyj-msxmX0557) （cstc2021jcyj-msxmX0557）
重庆市教育委员会科学技术研究项目(KJQN202300605) （KJQN202300605）
