486 citations · 2.3k across the 55 of their papers we have counts for
7 papers · 1 filter
Deep Item-based Collaborative Filtering for Top-N Recommendation
Feng Xue, Xiangnan He, Xiang Wang +3
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online per…
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
Xiangnan He, Jinhui Tang, Xiaoyu Du +3
Matrix factorization (MF) has been widely used to discover the low-rank structure and to predict the missing entries of data matrix. In many real-world learning systems, the data m…
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Le Wu, Peijie Sun, Richang Hong +3
Collaborative Filtering (CF) is one of the most successful approaches for recommender systems. With the emergence of online social networks, social recommendation has become a popu…
A Hierarchical Attention Model for Social Contextual Image Recommendation
Le Wu, Lei Chen, Richang Hong +3
Image based social networks are among the most popular social networking services in recent years. With tremendous images uploaded everyday, understanding users' preferences on use…
Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents
Bo Wang, Youjiang Xu, Yahong Han +1
Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is sti…
IGCV: Interleaved Structured Sparse Convolutional Neural Networks
Guotian Xie, Jingdong Wang, Ting Zhang +3
In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In add…
Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder
Jingkuan Song, Hanwang Zhang, Xiangpeng Li +3
Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video…