6 papers
ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling
Tianbao Ma, Chang Xi, Yichuan Zou +7
Accurate user modeling often depends on rich interaction histories, which are unavailable for billions of low-activity users. Large Language Models (LLMs) can infer latent user sta…
DREAM: Dynamic Refinement of Early Assignment Mappings
Liwei Guan, Huanjie Wang, Hongwei Zhang +2
Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While…
Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
Tianbao Ma, Ruochen Yang, Chengen Li +7
User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is req…
UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems
Mingming Ha, Guanchen Wang, Linxun Chen +9
In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommende…
PushGen: Push Notifications Generation with LLM
Shifu Bie, Jiangxia Cao, Zixiao Luo +9
We present PushGen, an automated framework for generating high-quality push notifications comparable to human-crafted content. With the rise of generative models, there is growing…
Selection and Exploitation of High-Quality Knowledge from Large Language Models for Recommendation
Guanchen Wang, Mingming Ha, Tianbao Ma +4
In recent years, there has been growing interest in leveraging the impressive generalization capabilities and reasoning ability of large language models (LLMs) to improve the perfo…