165 citations · 555 across the 19 of their papers we have counts for
5 papers · 1 filter
Collaborative Filtering with User-Item Co-Autoregressive Models
Chao Du, Chongxuan Li, Yin Zheng +2
Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the unde…
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Zhuxi Jiang, Yin Zheng, Huachun Tan +2
Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative…
Neural Autoregressive Collaborative Filtering for Implicit Feedback
Yin Zheng, Cailiang Liu, Bangsheng Tang +1
This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first conve…
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding +1
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model…
Document Neural Autoregressive Distribution Estimation
Stanislas Lauly, Yin Zheng, Alexandre Allauzen +1
We present an approach based on feed-forward neural networks for learning the distribution of textual documents. This approach is inspired by the Neural Autoregressive Distribution…