5 citations · 10 across the 7 of their papers we have counts for
6 papers · 1 filter
Precision Profile Pollution Attack on Sequential Recommenders via Influence Function
Xiaoyu Du, Yingying Chen, Yang Zhang +1
Sequential recommendation approaches have demonstrated remarkable proficiency in modeling user preferences. Nevertheless, they are susceptible to profile pollution attacks (PPA), w…
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
Xiaoyu Du, Xiangnan He, Fajie Yuan +3
As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, an…
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…
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…