Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (5): 803-814.DOI: 10.3778/j.issn.1673-9418.1905090

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Recommendation System Based on Users' Preference Mining Generative Adversarial Networks

LI Guangli, HUA Jin, YUAN Tian, ZHU Tao, WU Renzhong, JI Donghong, ZHANG Hongbin   

  1. 1. School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
    2. School of Software, East China Jiaotong University, Nanchang 330013, China
    3. School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
  • Online:2020-05-01 Published:2020-05-08



  1. 1. 华东交通大学 信息工程学院,南昌 330013
    2. 华东交通大学 软件学院,南昌 330013
    3. 武汉大学 国家网络安全学院,武汉 430072


Users' preference mining is one of the key issues in the research field of recommendation system, and it plays a very important role in improving the recommendation performance. Users' preference mining generative adversarial networks (UPM-GAN) is proposed to better analyze the implicit users' preference in the recommendation procedure from two aspects. On one hand, user-rating matrix is processed by the state-of-the-art triplet loss algorithm. It means better positive samples are obtained by the hard-negative mining procedure of the triplet loss algorithm, which will build a strong foundation for more accurately portraying users’ preference. On the other hand, SVD++ algorithm is utilized in turn to create the generation model of the UPM-GAN. The SVD++ algorithm can mine implicit users' preference by adding bias information and latent parameters. It helps improve the rating prediction accuracy of recommendation system. Finally, the state-of-the-art GAN framework is utilized to train the proposed recommendation system and experimental simulation is completed on two mainstream datasets: MovieLens-100K and MovieLens-1M. Experimental results demonstrate that the proposed UPM-GAN is superior to other baselines among all evaluation indices including Precision@K, mean average precision (MAP). Moreover, it has the advantages of faster convergence speed and stable training process. The proposed recommendation system based on UPM-GAN has very large practical value.

Key words: recommendation systems, generative adversarial networks (GAN), users' preference mining, singular value decomposition (SVD++), triplet loss, hard negative samples


用户偏好挖掘是推荐系统研究中的关键问题,它对于改善推荐质量具有非常重要的作用。提出用户偏好挖掘生成对抗网络(UPM-GAN),从两个角度深入分析用户隐含偏好:基于三元组损失算法对用户评分矩阵进行处理,挖掘难分负样本,以更好地确立正样本,为准确刻画用户偏好奠定基础;基于奇异值分解(SVD++)算法构建UPM-GAN的生成模型,利用SVD++算法中的偏置信息及隐式参数描述用户隐含偏好,  以提高评分预测精度。最后使用最新生成对抗网络(GAN)框架完成推荐系统训练,在MovieLens-100K、MovieLens-1M这两个主流数据集上展开实验仿真。实验表明UPM-GAN的Precision@K、均值平均精度(MAP)等多项指标均优于对比基线,且它还具有收敛速度快、训练过程平稳等优点。基于UPM-GAN的推荐系统具有一定实用价值。

关键词: 推荐系统, 生成对抗网络(GAN), 用户偏好挖掘, 奇异值分解(SVD++), 三元组损失, 难分负样本