Watch a 13-minute conference presentation from SIGIR 2024 exploring innovative approaches to non-overlapped multi-target cross-domain recommendations through mutual information-based preference disentangling and transferring techniques. Learn how researchers Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, and Mori Kurokawa tackle the challenges of cross-domain recommendation systems by developing methods to effectively transfer user preferences across different domains without overlapping items or users.
Mutual Information-based Preference Disentangling and Transferring for Cross-Domain Recommendations
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 W1.5 [fp] Mutual Information-based Preference Disentangling and Transferring
Taught by
Association for Computing Machinery (ACM)
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