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Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation

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

Tags

Recommendation Systems Courses Data Mining Courses Machine Learning Courses Information Retrieval Courses User Behavior Analysis Courses

Course Description

Overview

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Explore a 12-minute conference talk from SIGIR 2024 focused on behavior-contextualized item preference modeling for multi-behavior recommendation systems. Delve into the research presented by authors Mingshi Yan, Fan Liu, Jing Sun, Fuming Sun, Zhiyong Cheng, and Yahong Han as they discuss innovative approaches to enhancing recommendation algorithms. Gain insights into how incorporating multiple user behaviors and contextual information can improve the accuracy and effectiveness of recommendation systems. Learn about the latest advancements in this field and their potential applications in various domains.

Syllabus

SIGIR 2024 M3.5 [fp] Behavior-Contextualized Item Preference Modeling for Multi-Behavior Rec


Taught by

Association for Computing Machinery (ACM)

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