Assessment of autoencoder architectures for data representation
Efficient representation learning of data distribution is part and parcel of successful
execution of any machine learning based model. Autoencoders are good at learning the …
execution of any machine learning based model. Autoencoders are good at learning the …
基于深度混合模型评分推荐.
钱付兰， 李建红， 赵姝， 张燕平 - Journal of Nanjing …, 2019 - search.ebscohost.com
从用户‑项目评分矩阵中学习用户对项目的个性化偏好, 对于评分推荐来说至关重要.
许多推荐方法如潜在因子模型, 无法充分利用评分矩阵中的交互信息学到较好的个性化偏好而 …
许多推荐方法如潜在因子模型, 无法充分利用评分矩阵中的交互信息学到较好的个性化偏好而 …
Refined co-SVD recommender algorithm: data processing and performance metrics
A resurgence of research interest in recommender systems can be attributed to the widely
publicized Netflix competition with the grand prize of USD 1 million. The competition …
publicized Netflix competition with the grand prize of USD 1 million. The competition …
基于变分自编码器的评分预测模型.
陈海， 钱付兰， 陈洁， 赵姝… - Journal of Computer …, 2021 - search.ebscohost.com
深度学习模型具有鲁棒性差的局限性, 常见的如在图片中增加特定的噪声会影响到图片的分类和
预测结果. 近期有学者将深度学习引入到推荐系统中, 因此在推荐系统中也存在噪声对推荐精度 …
预测结果. 近期有学者将深度学习引入到推荐系统中, 因此在推荐系统中也存在噪声对推荐精度 …
HGAR: Hybrid granular algorithm for rating recommendation
F Qian, Y Huang, J Li, S Zhao, J Chen, X Wang… - … Sets: International Joint …, 2020 - Springer
Recommendation algorithms based on collaborative filtering show products which people
might like and play an important role in personalized service. Nevertheless, the most of them …
might like and play an important role in personalized service. Nevertheless, the most of them …