83 citations · 153 across the 12 of their papers we have counts for
12 papers
Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
Guanmeng Xian, Ning Yang, Philip S. Yu
Multimodal recommender systems exploit visual and textual signals to alleviate data sparsity, but this also makes them more vulnerable to evasion-based promotion attacks. Existing…
Denoised Recommendation Model with Collaborative Signal Decoupling
Zefeng Li, Ning Yang
Although the collaborative filtering (CF) algorithm has achieved remarkable performance in recommendation systems, it suffers from suboptimal recommendation performance due to nois…
Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation
Zhuhang Li, Ning Yang
Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items. Current recommender…
Contrastive Collaborative Filtering for Cold-Start Item Recommendation
Zhihui Zhou, Lilin Zhang, Ning Yang
The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content ont…
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Lilin Zhang, Ning Yang, Yanchao Sun +1
Adversarial training (AT) is widely considered as the most promising strategy to defend against adversarial attacks and has drawn increasing interest from researchers. However, the…
Time Lag Aware Sequential Recommendation
Lihua Chen, Ning Yang, Philip S Yu
Although a variety of methods have been proposed for sequential recommendation, it is still far from being well solved partly due to two challenges. First, the existing methods oft…
Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions
Ning Yang, Chao Tang, Yuhai Tu
Generalization is one of the most important problems in deep learning (DL). In the overparameterized regime in neural networks, there exist many low-loss solutions that fit the tra…
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
Xiaoyun Zhao, Ning Yang, Philip S. Yu
Cross-domain recommendation (CDR) has been attracting increasing attention of researchers for its ability to alleviate the data sparsity problem in recommender systems. However, th…
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning
Ziwen Du, Ning Yang, Zhonghua Yu +1
Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suff…
Dual Adversarial Variational Embedding for Robust Recommendation
Qiaomin Yi, Ning Yang, Philip S. Yu
Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and…
MLANE: Meta-Learning Based Adaptive Network Embedding
Chen Cui, Ning Yang, Philip S. Yu
Most existing random walk based network embedding methods often follow only one of two principles, homophily or structural equivalence. In real world networks, however, nodes exhib…
Deep Collaborative Embedding for information cascade prediction
Yuhui Zhao, Ning Yang, Tao Lin +1
Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing wo…