16 citations · 24 across the 19 of their papers we have counts for
13 papers · 1 filter
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment
Guorui Li, Dugang Liu, Lei Li +2
Large language model (LLM)-enhanced sequential recommendation typically aims to improve two core components: user semantic embedding extraction and utilization. Despite promising r…
FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
Jun Zhang, Dugang Liu, Xing Tang +2
Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods ad…
Data-Driven Function Calling Improvements in Large Language Model for Online Financial QA
Xing Tang, Hao Chen, Shiwei Li +7
Large language models (LLMs) have been incorporated into numerous industrial applications. Meanwhile, a vast array of API assets is scattered across various functions in the financ…
Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
Xing Tang, Ziqiang Cui, Jingyang Bin +6
The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often strugg…
Automated Information Flow Selection for Multi-scenario Multi-task Recommendation
Chaohua Yang, Dugang Liu, Shiwei Li +6
Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as c…
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Yunpeng Weng, Xing Tang +8
Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive p…
Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
Zexu Sun, Qiyu Han, Minqin Zhu +3
Improving user engagement and platform revenue is crucial for online marketing platforms. Uplift modeling is proposed to solve this problem, which applies different treatments (e.g…
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models
Kexin Zhang, Fuyuan Lyu, Xing Tang +5
The evolution of previous Click-Through Rate (CTR) models has mainly been driven by proposing complex components, whether shallow or deep, that are adept at modeling feature intera…
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems
Ziqiang Cui, Yunpeng Weng, Xing Tang +4
In recent years, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. Ho…
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
Zexu Sun, Hao Yang, Dugang Liu +3
In modern online platforms, incentives are essential factors that enhance user engagement and increase platform revenue. Over recent years, uplift modeling has been introduced as a…
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
Yunpeng Weng, Xing Tang, Zhenhao Xu +4
Customer Lifetime Value (CLTV) prediction is a critical task in business applications. Accurately predicting CLTV is challenging in real-world business scenarios, as the distributi…
Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
Xing Tang, Yang Qiao, Fuyuan Lyu +2
As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of pla…
Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms
Yunpeng Weng, Xing Tang, Liang Chen +2
FinTech platforms facilitated by digital payments are watching growth rapidly, which enable the distribution of mutual funds personalized to individual investors via mobile Apps. A…