YoVDO

Multimodality Invariant Learning for Multimedia-Based New Item Recommendation - Lecture 6

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

Tags

Recommender Systems Courses Data Mining Courses Machine Learning Courses Computer Vision Courses Information Retrieval Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a cutting-edge approach to multimedia-based new item recommendation in this 14-minute conference talk from SIGIR 2024. Delve into the concept of Multimodality Invariant Learning as presented by authors Haoyue Bai, Le Wu, Min Hou, Miaomiao Cai, Zhuangzhuang He, Yuyang Zhou, Richang Hong, and Meng Wang. Learn how this innovative technique addresses challenges in recommending new items with multimedia content, potentially revolutionizing recommendation systems for e-commerce, content platforms, and other digital services. Gain insights into the methodology, implementation, and potential applications of this advanced machine learning approach in the field of information retrieval and recommendation systems.

Syllabus

SIGIR 2024 M2.6 [fp] Multimodality Invariant Learning for Multimedia-Based New Item Recommendation


Taught by

Association for Computing Machinery (ACM)

Related Courses

Introduction to Recommender Systems
University of Minnesota via Coursera Text Retrieval and Search Engines
University of Illinois at Urbana-Champaign via Coursera Machine Learning: Recommender Systems & Dimensionality Reduction
University of Washington via Coursera Java Programming: Build a Recommendation System
Duke University via Coursera Introduction to Recommender Systems: Non-Personalized and Content-Based
University of Minnesota via Coursera