13 citations · 51 across the 17 of their papers we have counts for
18 papers
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
Xingyu Lu, Zhining Liu, Yanchu Guan +6
Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as new…
Multiple Instance Learning for Uplift Modeling
Yao Zhao, Haipeng Zhang, Shiwei Lyu +3
Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe ou…
Making Large Language Models Better Knowledge Miners for Online Marketing with Progressive Prompting Augmentation
Chunjing Gan, Dan Yang, Binbin Hu +6
Nowadays, the rapid development of mobile economy has promoted the flourishing of online marketing campaigns, whose success greatly hinges on the efficient matching between user pr…
ULMA: Unified Language Model Alignment with Human Demonstration and Point-wise Preference
Tianchi Cai, Xierui Song, Jiyan Jiang +3
Aligning language models to human expectations, e.g., being helpful and harmless, has become a pressing challenge for large language models. A typical alignment procedure consists…
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Lei Liu, Xiaoyan Yang, Yue Shen +4
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and re…
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Tianchi Cai, Jiyan Jiang, Wenpeng Zhang +7
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing b…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Tianchi Cai, Shenliao Bao, Jiyan Jiang +5
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most…
Leveraging Large Language Models for Pre-trained Recommender Systems
Zhixuan Chu, Hongyan Hao, Xin Ouyang +9
Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectivel…
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
Yan Wang, Zhixuan Chu, Xin Ouyang +10
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behavior…
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
Dan Yang, Binbin Hu, Xiaoyan Yang +4
With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users th…
Adversarial Learning for Incentive Optimization in Mobile Payment Marketing
Xuanying Chen, Zhining Liu, Li Yu +5
Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, i…
A framework for massive scale personalized promotion
Yitao Shen, Yue Wang, Xingyu Lu +6
Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popula…
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
Tianchi Cai, Wenpeng Zhang, Lihong Gu +2
Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that ca…
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
Yun Yue, Yongchao Liu, Suo Tong +7
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, Ada…
Graph Neural Network Based VC Investment Success Prediction
Shiwei Lyu, Shuai Ling, Kaihao Guo +5
Predicting the start-ups that will eventually succeed is essentially important for the venture capital business and worldwide policy makers, especially at an early stage such that…
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
Tianchi Cai, Daxi Cheng, Chen Liang +6
A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B tes…
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Ziqi Liu, Dong Wang, Qianyu Yu +8
Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited…