Hongyuan Zha

ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in …

Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. Most existing relevant models involve a feature extractor and a sentiment classifier, where the …

Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy

2021

The techniques based on the theory of differential privacy (DP) has become a standard building block in the machine learning community. DP training mechanisms offer strong guarantees that an adversary cannot determine with high confidence …

Learning Graphons via Structured Gromov-Wasserstein Barycenters

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory …

Sequential Recommendation with User Memory Networks

2018

User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches …