6 papers
Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin +4
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of ge…
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu +7
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation method…
LLM4Tag: Automatic Tagging System for Information Retrieval via Large Language Models
Ruiming Tang, Chenxu Zhu, Bo Chen +4
Tagging systems play an essential role in various information retrieval applications such as search engines and recommender systems. Recently, Large Language Models (LLMs) have bee…
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Rong Shan, Jiachen Zhu, Jianghao Lin +5
In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful informa…
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Chenxu Zhu, Shigang Quan, Bo Chen +7
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While le…
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Hangyu Wang, Jianghao Lin, Xiangyang Li +5
Click-through rate (CTR) prediction plays as a core function module in various personalized online services. The traditional ID-based models for CTR prediction take as inputs the o…