3.2k citations · 18.4k across the 142 of their papers we have counts for
18 papers · 1 filter
Counterfactual Critic Multi-Agent Training for Scene Graph Generation
Long Chen, Hanwang Zhang, Jun Xiao +3
Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understa…
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…
Attentive Aspect Modeling for Review-aware Recommendation
Xinyu Guan, Zhiyong Cheng, Xiangnan He +4
In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user…
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
Liping Li, Wei Xu, Tianyi Chen +2
In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine worker…
Enhancing Stock Movement Prediction with Adversarial Training
Fuli Feng, Huimin Chen, Xiangnan He +3
This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The…
Hierarchical Attention Network for Visually-aware Food Recommendation
Xiaoyan Gao, Fuli Feng, Xiangnan He +5
Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is i…
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data
Jingtao Ding, Guanghui Yu, Xiangnan He +2
Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largel…
NAIS: Neural Attentive Item Similarity Model for Recommendation
Xiangnan He, Zhankui He, Jingkuan Song +3
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency i…
Aesthetic-based Clothing Recommendation
Wenhui Yu, Huidi Zhang, Xiangnan He +3
Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decisio…
Generative Adversarial Active Learning for Unsupervised Outlier Detection
Yezheng Liu, Zhe Li, Chong Zhou +4
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classificati…
Temporal Relational Ranking for Stock Prediction
Fuli Feng, Xiangnan He, Xiang Wang +3
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on…
Learning to Recommend with Multiple Cascading Behaviors
Chen Gao, Xiangnan He, Dahua Gan +7
Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Perfor…
Adversarial Training Towards Robust Multimedia Recommender System
Jinhui Tang, Xiaoyu Du, Xiangnan He +3
With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to…
Outer Product-based Neural Collaborative Filtering
Xiangnan He, Xiaoyu Du, Xiang Wang +3
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the…
Adversarial Personalized Ranking for Recommendation
Xiangnan He, Zhankui He, Xiaoyu Du +1
Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Rankin…
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis +2
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interac…
Discrete Factorization Machines for Fast Feature-based Recommendation
Han Liu, Xiangnan He, Fuli Feng +3
User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expe…