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Lightweight Embeddings for Graph Collaborative Filtering - SIGIR 2024

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

Tags

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

Course Description

Overview

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Explore a cutting-edge approach to graph collaborative filtering in this 14-minute conference talk from SIGIR 2024. Delve into the concept of lightweight embeddings and their application in recommendation systems. Learn from authors Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, and Hongzhi Yin as they present their research on improving the efficiency and effectiveness of graph-based collaborative filtering techniques. Gain insights into how these lightweight embeddings can enhance recommendation accuracy while reducing computational complexity in large-scale systems.

Syllabus

SIGIR 2024 T1.4 [fp] Lightweight Embeddings for Graph Collaborative Filtering


Taught by

Association for Computing Machinery (ACM)

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