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.
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M3.5 [fp] Behavior-Contextualized Item Preference Modeling for Multi-Behavior Rec
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation - Session M3.5
-
Adaptive In-Context Learning with Large Language Models for Bundle Generation - Recommendation Systems
-
Modeling User Fatigue for Sequential Recommendation - Session M3.6
-
Configurable Fairness for New Item Recommendation Considering Entry Time - SIGIR 2024
-
Multimodality Invariant Learning for Multimedia-Based New Item Recommendation - Lecture 6
-
UniSAR: Modeling User Transition Behaviors between Search and Recommendation - Session M3.6
