Explore a critical analysis of next basket recommendation systems in this 16-minute conference talk from SIGIR 2024. Delve into the question of whether current approaches are truly achieving better beyond-accuracy performance. Join authors Ming Li, Yuanna Liu, Sami Jullien, Mozhdeh Ariannezhad, Andrew Yates, Mohammad Aliannejadi, and Maarten de Rijke as they examine the effectiveness of recommendation systems, focusing on metrics beyond traditional accuracy measures. Gain insights into the challenges and potential improvements in next basket recommendation algorithms, and understand the implications for real-world applications in e-commerce and personalized shopping experiences.
Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation - Session M3.5
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M3.5 [fp] Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Large Language Models for Next Point-of-Interest Recommendation - Tutorial 2.4
-
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
-
Disentangled Contrastive Hypergraph Learning for Next POI Recommendation - Lecture 4
-
Modeling User Fatigue for Sequential Recommendation - Session M3.6
-
Adaptive In-Context Learning with Large Language Models for Bundle Generation - Recommendation Systems
-
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning
