16 citations · 17 across the 11 of their papers we have counts for
12 papers
BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
Mengyang Ma, Xiaopeng Li, Wanyu Wang +9
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements al…
Less Is More: Elevating RAG via Performance-Driven Context Compression
Ziqiang Cui, Yunpeng Weng, Xing Tang +7
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for improving the timeliness of knowledge updates and the factual accuracy of large language models. Howeve…
Timing is Important: Risk-aware Fund Allocation based on Time-Series Forecasting
Fuyuan Lyu, Linfeng Du, Yunpeng Weng +6
Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. N…
A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
Xing Tang, Yunpeng Weng, Fuyuan Lyu +2
With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under co…
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…
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…
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
Bowei He, Yunpeng Weng, Xing Tang +5
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individ…
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…
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
Yunpeng Weng, Xing Tang, Liang Chen +1
Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of…
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
Yunpeng Weng, Xu Chen, Liang Chen +1
Graph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Graph…