Summary of Multi-treatment Multi-task Uplift Modeling For Enhancing User Growth, by Yuxiang Wei et al.
Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth
by Yuxiang Wei, Zhaoxin Qiu, Yingjie Li, Yuke Sun, Xiaoling Li
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Treatment Multi-Task (MTMT) uplift network aims to estimate treatment effects in a multi-task scenario, enhancing business outcomes by measuring individual user responses to various treatments in online games. Building on previous research that typically considers single-task and single-treatment settings, MTMT addresses the multi-treatment problem as a causal inference issue with tiered responses. The model consists of a user feature encoder using a multi-gate mixture of experts (MMOE) network to learn inter-task relations, and a treatment-user feature interaction module to capture correlations between treatments and user features. This enables separate measurement of base and incremental treatment effects for each task based on treatment-aware representations. Experimental results demonstrate MTMT’s effectiveness in single/multi-treatment and single/multi-task settings, with the model being deployed in an online gaming platform to improve user experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand how people react to different treatments in online games. The goal is to use this information to make the games more enjoyable for users. Currently, most research in this area only looks at one type of treatment and how it affects people’s behavior. This paper takes it a step further by considering multiple types of treatments and multiple tasks that users can perform. To do this, the authors developed a new model called MTMT that can handle these complex situations. The model is tested on two different datasets and shows promising results. |
Keywords
» Artificial intelligence » Encoder » Inference » Mixture of experts » Multi task
