2 citations · 3 across the 4 of their papers we have counts for
5 papers
TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation
Zhifei Zheng, Yunfei Liu, Bin Liu +7
Applying scaling laws to recommendation retrieval is hindered by feature heterogeneity: naively stacking Transformer layers yields diminishing returns because heterogeneous fields…
Think before Recommendation: Autonomous Reasoning-enhanced Recommender
Xiaoyu Kong, Junguang Jiang, Bin Liu +6
The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…
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…
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…
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
Junguang Jiang, Yanwen Huang, Bin Liu +6
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures. We demonstrate t…