YoVDO

Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering

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

Tags

Recommender Systems Courses Data Mining Courses Machine Learning Courses Neural Networks Courses Information Retrieval Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the individuality and collectivity of intents behind interactions for graph collaborative filtering in this 15-minute conference talk presented at SIGIR 2024. Delve into the research conducted by Yi Zhang, Lei Sang, and Yiwen Zhang as they discuss their findings on improving graph-based recommendation systems. Gain insights into how understanding user intents, both individually and collectively, can enhance the performance of collaborative filtering algorithms in graph structures. Learn about the latest advancements in this field and their potential applications in various recommendation scenarios.

Syllabus

SIGIR 2024 T1.4 [fp] Exploring the Individuality and Collectivity of Intents behind Interactions


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