Xiaolin Zheng

Collaborative Autoencoder for Recommender Systems

2017 · arXiv (Cornell University)

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing works. First, most works …

Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

2020 · arXiv (Cornell University)

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models …

DAPrompt: Dual Alignment Prompt of Structure and Semantics for Few-shot Graph Learning

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Few-shot graph learning remains a fundamental yet challenging problem, especially under heterophilic graph settings where connected nodes are likely to belong to different classes. In such scenarios, two key challenges arise: (1) unreliable or noisy …

DTCDR

2019

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain …

A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

2020

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed …