Predicting User Spending on Newly Downloaded Mobile Games under Consumption Uncertainty

Conceitos Básicos

Accurately predicting user spending on newly downloaded mobile games is crucial for maximizing revenue, but the inherently unpredictable nature of user behavior poses significant challenges. This work proposes a robust model training and evaluation framework to mitigate label variance and extremes, and introduces a collaborative-enhanced model that can predict user spending without relying on user IDs, ensuring user privacy and enabling seamless online training.

Resumo

The paper addresses the challenge of accurately predicting user spending on newly downloaded mobile games, which is crucial for maximizing revenue. The authors propose a two-part solution:

A robust model training and evaluation framework:

Standardizes spending data to mitigate label variance and extremes, ensuring stability in the modeling process.
Employs a regression loss function and prioritizes model stability and evaluation certainty.
Utilizes a ranking-based metric to evaluate the model's ability to predict which games users have downloaded, rather than the exact spending amount.

A collaborative-enhanced model:

Represents user preferences and game features separately before merging them as input to the spending prediction module.
Does not rely on user IDs, ensuring user privacy and enabling seamless online training.
Combines the collaborative signal with the existing production model to enhance spending prediction.

Through rigorous experimentation, the authors demonstrate that their approach outperforms production models, achieving a 17.11% enhancement on offline data and a 50.65% boost in an online A/B test.

Estatísticas

The dataset contains many zero values, high label variance in non-zero values, and extremely high values, which can disrupt model optimization and performance.
The average consumption cost is 227.16, with a standard deviation of 2130.06, indicating substantial variability within non-zero consumption costs.
The maximum consumption cost reaches an extreme value of 421,387, highlighting the presence of outliers with exceptionally high spending.

Citações

"Accurately predicting user spending on newly downloaded mobile games has become paramount for maximizing revenue."
"The inherently unpredictable nature of user behavior poses significant challenges in this endeavor."
"Our collaborative-enhanced model has proven effective in two settings: First, on the offline business dataset, it outperformed the production model by 17.11% in the 30-day spending money prediction task. Second, in the online A/B test, it achieved a 50.65% improvement in user payment revenue over two weeks compared to the production model."

Principais Insights Extraídos De

Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty

by Peijie Sun,Y... às arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08301.pdf

Perguntas Mais Profundas

How can the proposed framework and collaborative-enhanced model be extended to other domains beyond mobile gaming, such as e-commerce or streaming platforms, where user spending prediction is also crucial?

The proposed framework and collaborative-enhanced model can be extended to other domains by adapting the model architecture and training process to suit the specific characteristics of the new domain. For e-commerce, the model can be modified to incorporate user browsing history, purchase behavior, and product preferences to predict future spending on different products. In the case of streaming platforms, user interaction with content, viewing history, and subscription patterns can be used to predict user spending on premium content or subscriptions. By customizing the input features and training data to align with the domain-specific user behavior, the framework can be applied effectively to various industries where user spending prediction is essential.

What are the potential limitations or drawbacks of the collaborative signal modeling approach, and how could they be addressed to further improve the model's performance?

One potential limitation of the collaborative signal modeling approach is the reliance on historical user interactions, which may not always capture the evolving preferences and behaviors of users. This could lead to model stagnation and reduced accuracy over time. To address this limitation, the model could be enhanced by incorporating real-time user feedback and interactions to adapt to changing user preferences. Additionally, incorporating contextual information such as user demographics, external factors, and current trends could help improve the model's performance by providing a more comprehensive understanding of user behavior. Regular model retraining and updating based on the latest data can also help mitigate the impact of model stagnation and ensure continued accuracy.

Given the dynamic nature of user behavior and game preferences, how could the proposed framework and model be adapted to handle concept drift and continuously update the predictions over time?

To handle concept drift and continuously update predictions over time, the proposed framework and model can implement adaptive learning techniques that allow the model to adjust to changing patterns in user behavior and preferences. This can be achieved through techniques such as online learning, where the model is updated incrementally as new data becomes available, allowing it to adapt to evolving trends and patterns. Additionally, incorporating feedback loops that monitor model performance and trigger retraining when significant changes are detected can help ensure the model remains accurate and up-to-date. By implementing mechanisms for continuous monitoring, evaluation, and adaptation, the framework can effectively handle concept drift and maintain prediction accuracy over time.