Explore a cutting-edge conference talk on enhancing sequential recommender systems using augmented knowledge from aligned Large Language Models (LLMs). Delve into the research presented by Yankun Ren, Zhongde Chen, Xinxing Yang, Longfei Li, Cong Jiang, Lei Cheng, Bo Zhang, Linjian Mo, and Jun Zhou at the Association for Computing Machinery (ACM) SIGIR 2024 conference. Learn how the integration of LLMs can improve the performance and capabilities of sequential recommenders, potentially revolutionizing personalized content delivery and user experience in various applications. Gain insights into the latest advancements in the intersection of recommendation systems and natural language processing during this 16-minute presentation.
Enhancing Sequential Recommenders with Augmented Knowledge from Aligned LLMs - SIGIR 2024
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.6 [fp] Enhancing Sequential Recommenders with Augmented Knowledge from Aligned LLMs
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Sequential Recommendation with Latent Relations Based on Large Language Models - M1.6
-
Modeling User Fatigue for Sequential Recommendation - Session M3.6
-
Reinforcement Learning-based Recommender Systems with Large Language Models - SIGIR 2024
-
Data-efficient Fine-tuning for LLM-based Recommendation - SIGIR 2024 M1.6
-
Large Language Models as Learnable Planners for Long-Term Recommendation
-
Generative AI Engineering with LLMs
