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Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization - Lecture 1

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

Tags

Recommender Systems Courses Data Mining Courses Machine Learning Courses Information Theory Courses Information Retrieval Courses Collaborative Filtering Courses Explainable AI Courses

Course Description

Overview

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Explore a cutting-edge approach to sequential recommendation systems in this 19-minute conference talk from SIGIR 2024. Delve into the innovative method of collaborative explanation through mutual information maximization, presented by researchers Yi Yu, Kazunari Sugiyama, and Adam Jatowt. Gain insights into how this technique enhances explainability in search and recommendation systems, potentially revolutionizing user experience and system transparency. Learn about the intersection of sequential recommendation, collaborative filtering, and information theory in this concise yet informative presentation from the Association for Computing Machinery (ACM).

Syllabus

SIGIR 2024 T1.1 [fp] Sequential Rec Collaboration Explanation via Mutual Info Maximization


Taught by

Association for Computing Machinery (ACM)

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