Name
Abstract | ||
|---|---|---|
Online advertising campaigns are typically launched for a customer across multiple touch points (scenarios) before the conversion of his final purchase. To maximize the advertisers' revenue, it requires the platform to develop its advertising strategy based on the consumers' behavioral trajectories in the previous scenarios. Meanwhile, it is also critical to maintain the interpretability of the models on the conversion rate; however, modern reinforcement learning based solutions fail to do so due to their black-box modeling on the consumer intents. In this paper, we model consumer's purchase intention as a latent variable, and formulate the advertising problem as a partially observed Markov Decision Process (POMDP). We incorporate the expectation maximization (EM) algorithms for solving the optimal POMDP. Our extensive experiments based on large-scale real-world data demonstrate that our method provides superior performance over several baselines. Apart from the improved advertising performance, our method is able to offer interpretation on the attribution of the conversion.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.5555/3398761.3399043 | AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
Auckland
New Zealand
May, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7518-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaoqing Peng | 1 | 0 | 1.01 |
| junqi jin | 2 | 118 | 7.95 |
| Lan Luo | 3 | 6 | 1.19 |
| Yaodong Yang | 4 | 41 | 11.92 |
| Rui Luo | 5 | 29 | 3.78 |
| Jun Wang | 6 | 2514 | 138.37 |
| Weinan Zhang | 7 | 1228 | 97.24 |
| Xu Miao | 8 | 1 | 1.70 |
| Chuan Yu | 9 | 7 | 5.46 |
| Tiejian Luo | 10 | 120 | 26.96 |
| Han Li | 11 | 127 | 6.98 |
| Jian Xu | 12 | 0 | 2.03 |
| Kun Gai | 13 | 312 | 20.61 |