Search Results for author:
Found 9 papers, 3 papers with code
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news.
Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
In real-world applications, events are not always observable, and estimating event time is as important as predicting future events.
Requirements Elicitation in Cognitive Service for Recommendation
To this end, two phases must be applied: I. Sequence planning and Real-time detection of user requirement, II. Service resource selection and Response generation.
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation
The fundamental problem with these studies is that they ignore the evolution of services over time and the representation gap between services and requirements.
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism
However, relying only on aggregation to propagate information in dynamic graphs can result in delays in information propagation and thus affect the performance of the method.
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services
In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services.
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge
Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags.
An Improved Historical Embedding without Alignment
Our method outperformed three other popular methods in terms of the number of words correctly identified to have changed in meaning.
A Fast Greedy Algorithm for Outlier Mining
The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data.
