Explore an empirical analysis of multi-turn conversational recommender systems in this 14-minute conference talk presented at SIGIR 2024. Delve into the research conducted by authors Lu Zhang, Chen Li, Yu Lei, Zhu Sun, and Guanfeng Liu as they examine the intricacies of conversational IR and recommendation systems. Gain insights into the latest advancements and challenges in developing effective multi-turn dialogue-based recommendation algorithms. Learn about the methodologies employed, key findings, and potential implications for improving user experiences in conversational AI and personalized recommendation technologies.
An Empirical Analysis on Multi-turn Conversational Recommender Systems - Lecture 3.2
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M3.2 [rr] An Empirical Analysis on Multi-turn Conversational Recommender Systems
Taught by
Association for Computing Machinery (ACM)
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