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Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

In this presentation, a novel approach to anomaly detection using Graph Neural Networks (GNNs) is introduced. This method models multivariate time series as a graph, learns sensor relationships and detects anomalies by identifying deviations from these learned patterns. By capturing both temporal dependencies and sensor interactions, proposed approach enhances anomaly detection, offering superior performance compared to traditional techniques.

Sentimentally enhanced conversation recommender system

A conversation recommender system (CRS) aims to provide high-quality recommendations to users in fewer conversation turns. Existing studies often rely on knowledge graphs and entities mentioned in dialogues to enhance the representation of entity information. However, they fail to thoroughly explore users’ emotional tendencies toward entities or effectively differentiate the varying impacts of different entities on user preferences. Recently, Liu et.al. proposed an innovative Sentimentally Enhanced Conversation Recommender system (SECR). In this presentation, the SECR method will be explained and described how this method captured users' emotional inclinations toward entities.