Watch a 14-minute conference presentation from SIGIR 2024 exploring the disentanglement of ID and modality effects in session-based recommendation systems. Learn how researchers Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang, Hongfei Lin and Fenglong Ma investigate methods to separate and analyze the distinct impacts of item identifiers and modalities in sequential recommendation scenarios. Gain insights into their approach for improving recommendation accuracy by better understanding these two key components that influence user preferences and behaviors during online sessions.
Disentangling ID and Modality Effects for Session-based Recommendation
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 W1.2 [fp] Disentangling ID and Modality Effects for Session-based Recommendation
Taught by
Association for Computing Machinery (ACM)
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