Disentangled Contrastive Hypergraph Learning for Next POI Recommendation - Lecture 4
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
Explore cutting-edge research in point-of-interest (POI) recommendation systems through this conference talk presented at SIGIR 2024. Delve into the innovative approach of disentangled contrastive hypergraph learning for next POI recommendation, as presented by authors Yantong Lai, Yijun Su, Lingwei Wei, Tianqi He, Haitao Wang, Gaode Chen, Daren Zha, Qiang Liu, and Xingxing Wang. Gain insights into advanced techniques for improving location-based recommendations and understand how hypergraph structures and contrastive learning methods can enhance the accuracy and relevance of POI suggestions.
Syllabus
SIGIR 2024 T2.4 [fp] Disentangled Contrastive Hypergraph Learning for Next POI Recommendation
Taught by
Association for Computing Machinery (ACM)
Related Courses
Stanford Seminar - Audio Research: Transformers for Applications in Audio, Speech and MusicStanford University via YouTube How to Represent Part-Whole Hierarchies in a Neural Network - Geoff Hinton's Paper Explained
Yannic Kilcher via YouTube OpenAI CLIP - Connecting Text and Images - Paper Explained
Aleksa Gordić - The AI Epiphany via YouTube Learning Compact Representation with Less Labeled Data from Sensors
tinyML via YouTube Human Activity Recognition - Learning with Less Labels and Privacy Preservation
University of Central Florida via YouTube