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Content-based Graph Reconstruction for Cold-start Item Recommendation - 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 Content-Based Filtering Courses Collaborative Filtering Courses

Course Description

Overview

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Explore a 13-minute conference talk from SIGIR 2024 that delves into content-based graph reconstruction for cold-start item recommendation. Learn about innovative approaches presented by authors Jinri Kim, Eungi Kim, Kwangeun Yeo, Yujin Jeon, Chanwoo Kim, Sewon Lee, and Joonseok Lee as they address challenges in recommender systems. Gain insights into graph-based techniques and their application in solving the cold-start problem, a common issue in recommendation algorithms for new items with limited user interaction data.

Syllabus

SIGIR 2024 T1.4 [fp] Content-based Graph Reconstruction for Cold-start item recommendation


Taught by

Association for Computing Machinery (ACM)

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