2 citations · 2 across the 4 of their papers we have counts for
5 papers
RecGPT-V3 Technical Report
Bowen Zheng, Chao Yi, Dian Chen +26
Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…
LLM-Auction: Generative Auction towards LLM-Native Advertising
Chujie Zhao, Qun Hu, Shiping Song +4
The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising paradigm by integrating ads into LLM-gener…
Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang +7
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising
Bin Liu, Yunfei Liu, Ziru Xu +4
Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate traffic based on eCPM (predicte…