Search Results for author:
Found 4 papers, 2 papers with code
GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches.
Ranked #1 on
Movie Recommendation
on MovieLens 1M
(RMSE metric)
Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning.
