8 papers
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Deyao Hong, Kehan Zheng, Qian Li +3
Online recommendation has traditionally taken place after a user enters a platform, which determines the candidate pool and the ranking shown to the user. LLM-based user agents ena…
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
Ningyuan Xi, Yetao Wu, Kun Fan +3
Large Language Models (LLM) often need to be Continual Pre-Trained (CPT) to obtain unfamiliar language skills or adapt to new domains. The huge training cost of CPT often asks for…
S-GRec: Personalized Semantic-Aware Generative Recommendation with Asymmetric Advantage
Jie Jiang, Hongbo Tang, Wenjie Wu +6
Generative recommendation models sequence generation to produce items end-to-end, but training from behavioral logs often provides weak supervision on underlying user intent. Altho…
SCoTER: Structured Chain-of-Thought Transfer for Enhanced Recommendation
Jie Jiang, Yang Wu, Qian Li +7
Harnessing the reasoning power of Large Language Models (LLMs) for recommender systems is hindered by two fundamental challenges. First, current approaches lack a mechanism for aut…
Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
Kehan Zheng, Deyao Hong, Qian Li +4
Recommender systems are tasked to infer users' evolving preferences and rank items aligned with their intents, which calls for in-depth reasoning beyond pattern-based scoring. Rece…
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation
Yang Wu, Haoze Wang, Qian Li +3
Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent. However, effectively integra…