YoVDO

DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation - Lecture 4

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

Recommender Systems Courses Machine Learning Courses Graph Theory Courses Information Retrieval Courses Hypergraphs Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 10-minute conference talk from the Association for Computing Machinery (ACM) that delves into the innovative DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation. Learn about this cutting-edge approach presented by authors Yingqi Zhao, Haiwei Zhang, Qijie Bai, Changli Nie, and Xiaojie Yuan as part of the Graphs and RecSys (M3.4) session. Gain insights into how this novel technique combines hypergraph structures with masked autoencoders to enhance group recommendation systems, potentially revolutionizing the field of collaborative filtering and personalized content delivery for groups.

Syllabus

SIGIR 2024 M3.4 [fp] DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation


Taught by

Association for Computing Machinery (ACM)

Related Courses

Aplicaciones de la teoría de grafos a la vida real
Miríadax Aplicaciones de la Teoría de Grafos a la vida real
Universitat Politècnica de València via UPV [X] Introduction to Computational Thinking and Data Science
Massachusetts Institute of Technology via edX Genome Sequencing (Bioinformatics II)
University of California, San Diego via Coursera Algorithmic Information Dynamics: From Networks to Cells
Santa Fe Institute via Complexity Explorer