山西大学学报（自然科学版）2024，Vol.47Issue(3)：481-493,13.DOI:10.13451/j.sxu.ns.2024005
基于对抗型排序学习的混合推荐算法
Hybrid Recommendation Algorithm Based on Adversarial Learning-to-rank
摘要
Abstract
The recommendation system can help users filter the massive amount of information.Every single recommendation algo-rithm has some defects.Mixed recommendation based on deep learning can effectively alleviate the problem of sparse data in tradi-tional recommendation algorithms by incorporating auxiliary information and often achieve better results.In most current studies,specific auxiliary information is used for different algorithms,but there is no unified hybrid recommendation framework.This paper proposed a hybrid recommendation algorithm based on adversarial learning-to-rank:MRecGAN.The idea of learning-to-rank was used to integrate multiple basic recommendation algorithms,built unified auxiliary data,and digged the deep relationship among fea-tures.It used generative adversarial networks to learn the sorting function,improved the performance of one discriminator and two generators,and obtained the recommendation sequence.Finally,the real Movielens dataset combined with auxiliary data was used for testing.The experimental results show that the model integrates the advantages of the basic models better.NDCG and other in-dexes improve significantly,MRR improves by 32.05%compared to CML.关键词
对抗网络/辅助信息/数据稀疏/Movielens/NDCGKey words
adversarial networks/auxiliary information/data sparsity/Movielens/NDCG分类
信息技术与安全科学引用本文复制引用
许侃,吴鑫卓,林原,顾茜,林鸿飞,谢张..基于对抗型排序学习的混合推荐算法[J].山西大学学报（自然科学版）,2024,47(3):481-493,13.基金项目
国家社会科学基金(20BTQ074) （20BTQ074）
