Guodong Long

NeuRec: On Nonlinear Transformation for Personalized Ranking

2018

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity …

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

2019 · arXiv (Cornell University)

We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several …

Competitive and Cooperative Heterogeneous Deep Reinforcement Learning

2020

Numerous deep reinforcement learning methods have been proposed, including deterministic, stochastic, and evolutionary-based hybrid methods. However, among these various methodologies, there is no clear winner that consistently outperforms the others in every task in terms …

Federated Intelligence in Web: A Tutorial

2025

The recent development of Web Intelligence has heightened privacy concerns among end-users. Federated intelligence offers a novel approach to restructuring Web Intelligence within a federated setting to better protect privacy. Additionally, the advent of large …

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable …

Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

2021

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them (a.k.a. knowledge graph completion). Prevalent graph embedding approaches, e.g., TransE, learn structured …

Multi-center federated learning: clients clustering for better personalization

2022 · World Wide Web

Abstract Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy …