Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation

TL;DR

This paper introduces a multi-task representation learning approach for robust online budget allocation in marketing, leveraging clustering of hidden representations to improve scalability and noise robustness, validated through offline and online experiments.

Contribution

It proposes a novel multi-task representation network combined with clustering to reformulate budget allocation, enabling scalable and noise-robust optimization in industrial scenarios.

Findings

Outperforms six state-of-the-art algorithms offline.

Achieves 0.53% and 0.65% improvements in order volume and GMV online.

Effective in large-scale noisy data environments.

Abstract

Marketing optimization, commonly formulated as an online budget allocation problem, has emerged as a pivotal factor in driving user growth. Most existing research addresses this problem by following the principle of 'first predict then optimize' for each individual, which presents challenges related to large-scale counterfactual prediction and solving complexity trade-offs. Note that the practical data quality is uncontrollable, and the solving scale tends to be tens of millions. Therefore, the existing approaches make the robust budget allocation non-trivial, especially in industrial scenarios with considerable data noise. To this end, this paper proposes a novel approach that solves the problem from the cluster perspective. Specifically, we propose a multi-task representation network to learn the inherent attributes of individuals and project the original features into high-dimension hidden representations through the first two layers of the trained network. Then, we divide these hidden representations into $K$ groups through partitioning-based clustering, thus reformulating the problem as an integer stochastic programming problem under different total budgets. Finally, we distill the representation module and clustering model into a multi-category model to facilitate online deployment. Offline experiments validate the effectiveness and superiority of our approach compared to six state-of-the-art marketing optimization algorithms. Online A/B tests on the Meituan platform indicate that the approach outperforms the online algorithm by 0.53% and 0.65%, considering order volume (OV) and gross merchandise volume (GMV), respectively.