32 citations · 37 across the 12 of their papers we have counts for
12 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…
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
Mingming Ha, Xuewen Tao, Wenfang Lin +3
In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values and those missing values are gene…
Integrating Large Language Models with Graphical Session-Based Recommendation
Naicheng Guo, Hongwei Cheng, Qianqiao Liang +2
With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pione…
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
Wujiang Xu, Xuying Ning, Wenfang Lin +7
Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches a…
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang +5
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity and cold-start problems present in Single-Domain Sequential Recommendation (SDSR). Existing CD…
Neural Node Matching for Multi-Target Cross Domain Recommendation
Wujiang Xu, Shaoshuai Li, Mingming Ha +5
Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) s…
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han +3
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…