In this research, we proposed an autoencoder based collaborative filtering method, in which pretraining and stacking mechanism is provided. The experimental study on commonly used MovieLens datasets have shown its potential and effectiveness in getting higher recall....
The problem is akin to that of collaborative filtering; therein the ratings matrix is partially observed and the goal is to estimate the complete matrix given the partially observed ones. Our problem is to recover the complete gene expression matrix by imputing the dropouts. Traditional strategies ...
This work addresses the problem of cold and warm start arising in recommender systems. Usually a latent factor model based on matrix factorization is used for collaborative filtering (warm start recommender system). Only in recent times, a handful of papers have been published that uses auto...
Network embedding aims to represent vertices in the network with low-dimensional dense real number vectors, so that the attained vertices can acquire the ability of representation and inference in vector space. With the expansion of the scale of complex networks, how to make the high-dimensional n...
模型分为user-based AutoRec和item-based AutoRec两种。 item-based AutoRec 输入 n:物品数 i=1...n m:用户数 input AE模型 output AutoRec在传统AE的基础上做了如下变化 损失函数只和观察到的元素有关 加上正则化项 目标函数 预测 基线:RBM-CF
recommender system;variational autoencoders;deep learning;collaborative filtering;multi-criteria 1. Introduction The advancement of online resources has led to a significant increase in online information, resulting in information overload that complicates users’ ability to acquire the items, information, ...
latent-factor collaborative fltering 中常用的两种似然函数: gaussian likelihoods logistic likelihoods 原因: 多项式似然非常适合于隐式反馈数据的建模,并且更接近 rank loss; 无论数据的稀缺性如何,采用principled Bayesian方法都更加稳健。 为目标函数引入了一个不同的正则化参数,并使用退火调整参数。
A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance Inform Fusion (2021) G. Han et al. Virtual current coefficients based power transistors fault diagnosis for small power EV-SRM drives IEEE Trans Transp Electrif (2021) H. Ruan et al. A relation-based semi...
2014. Autoencoder- based collaborative filtering. In International Conference on Neural Information Processing. Springer, 284-291.Ouyang, Y., Liu, W., Rong, W. & Xiong, Z. Autoencoder-based collaborative filtering. In International Conference on Neural Information Processing, 284-291 (Springer, ...
collaborative filteringdeep learningrecommender systemAs the expansion of Internet, the recommender system is attracting the attention of many industry engineers and researcher, especially the collaborating filtering recommender system. However, there are still some challenges. For example, the sparse feature...