Direct heterogeneous causal learning for resource allocation problems in marketing
H Zhou, S Li, G Jiang, J Zheng, D Wang - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Marketing is an important mechanism to increase user engagement and improve platform
revenue, and heterogeneous causal learning can help develop more effective strategies …
revenue, and heterogeneous causal learning can help develop more effective strategies …
Safe offline reinforcement learning with real-time budget constraints
Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL),
research on safe RL has made significant progress in recent years. However, most existing …
research on safe RL has made significant progress in recent years. However, most existing …
An end-to-end framework for marketing effectiveness optimization under budget constraint
Online platforms often incentivize consumers to improve user engagement and platform
revenue. Since different consumers might respond differently to incentives, individual-level …
revenue. Since different consumers might respond differently to incentives, individual-level …
Decision focused causal learning for direct counterfactual marketing optimization
H Zhou, R Huang, S Li, G Jiang, J Zheng… - Proceedings of the 30th …, 2024 - dl.acm.org
Marketing optimization plays an important role to enhance user engagement in online
Internet platforms. Existing studies usually formulate this problem as a budget allocation …
Internet platforms. Existing studies usually formulate this problem as a budget allocation …
End-to-end cost-effective incentive recommendation under budget constraint with uplift modeling
In modern online platforms, incentives (eg, discounts, bonus) are essential factors that
enhance user engagement and increase platform revenue. Over recent years, uplift …
enhance user engagement and increase platform revenue. Over recent years, uplift …
Rl-mpca: A reinforcement learning based multi-phase computation allocation approach for recommender systems
J Zhou, S Mao, G Yang, B Tang, Q **e, L Lin… - Proceedings of the …, 2023 - dl.acm.org
Recommender systems aim to recommend the most suitable items to users from a large
number of candidates. Their computation cost grows as the number of user requests and the …
number of candidates. Their computation cost grows as the number of user requests and the …
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding
S He, C Yu, Q Lin, S Mao, B Tang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers
employ strategic bidding to optimize their advertising impact while adhering to various …
employ strategic bidding to optimize their advertising impact while adhering to various …
Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving
technology within the Internet of Vehicles (IoV), presents significant challenges to the …
technology within the Internet of Vehicles (IoV), presents significant challenges to the …
Real-time bidding with multi-agent reinforcement learning in multi-channel display advertising
C Chen, G Wang, B Liu, S Song, K Mao, S Yu… - Neural Computing and …, 2024 - Springer
Real-time bidding is the main way to display advertisements in the current e-commerce
market. To maximize the revenue and investment reporting (ROI) brought by advertising, the …
market. To maximize the revenue and investment reporting (ROI) brought by advertising, the …
Deep Isotonic Embedding Network: A flexible Monotonic Neural Network
Guaranteeing the monotonicity of a learned model is crucial to address concerns such as
fairness, interpretability, and generalization. This paper develops a new monotonic neural …
fairness, interpretability, and generalization. This paper develops a new monotonic neural …