Explore a cutting-edge conference talk on leveraging Large Language Models (LLMs) for intent-driven session recommendations in the field of recommender systems. Delve into the research presented by authors Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, and Yew Soon Ong at the Association for Computing Machinery (ACM) SIGIR 2024 conference. Learn how LLMs are being applied to enhance the accuracy and relevance of session-based recommendations by understanding user intent. Gain insights into the latest advancements in combining RecSys and LLMs to improve personalized content delivery and user experience. This 15-minute presentation offers a concise yet comprehensive overview of the innovative approaches being developed at the intersection of natural language processing and recommendation systems.
Large Language Models for Intent-Driven Session Recommendations - Session M1.6
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.6 [fp] Large Language Models for Intent-Driven Session Recommendations
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Sequential Recommendation with Latent Relations Based on Large Language Models - M1.6
-
Adaptive In-Context Learning with Large Language Models for Bundle Generation - Recommendation Systems
-
Large Language Models with Semantic Search
-
Large Language Models as Learnable Planners for Long-Term Recommendation
-
Reinforcement Learning-based Recommender Systems with Large Language Models - SIGIR 2024
-
Introducing Large Language Models Course (How To)
