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

• URL: http://arxiv.org/abs/2404.08301v1
• Date: Fri, 12 Apr 2024 07:47:02 GMT
• Title: Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
• Authors: Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, Meng Wang,
• Abstract summary: We propose a robust model training and evaluation framework to mitigate label variance and extremes. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs. Our approach demonstrates notable improvements over production models, achieving a remarkable textbf17.11% enhancement on offline data.
• Score: 49.431361908465036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending data to mitigate label variance and extremes, ensuring stability in the modeling process. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs, thus ensuring user privacy and enabling seamless online training. Our model adopts a unique approach by separately representing user preferences and game features before merging them as input to the spending prediction module. Through rigorous experimentation, our approach demonstrates notable improvements over production models, achieving a remarkable \textbf{17.11}\% enhancement on offline data and an impressive \textbf{50.65}\% boost in an online A/B test. In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.

Related papers

• Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout [3.187236205541292]
  We propose a novel approach that enhances the architecture of purely neural network models by incorporating the Monte Carlo Dropout (MCD) framework. We benchmarked the proposed method using data from one of the most downloaded mobile games in the world. Our approach provides confidence metric as an extra dimension for performance evaluation across various neural network models.
  arXiv  Detail & Related papers  (2024-11-24T18:14:44Z)
• Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming [0.5755004576310334]
  We discuss the problem of predicting the user's propensity to spend in a gaming round, so it can be utilized for various downstream applications. e.g. upselling users by incentivizing them marginally as per their spending propensity, or personalizing the product listing based on the user's propensity to spend. We show that our proposed architecture outperforms the existing models on the task of predicting the user's propensity to spend in a gaming round.
  arXiv  Detail & Related papers  (2024-09-25T16:40:51Z)
• Adversarial Robustification via Text-to-Image Diffusion Models [56.37291240867549]
  Adrial robustness has been conventionally believed as a challenging property to encode for neural networks. We develop a scalable and model-agnostic solution to achieve adversarial robustness without using any data.
  arXiv  Detail & Related papers  (2024-07-26T10:49:14Z)
• New User Event Prediction Through the Lens of Causal Inference [20.676353189313737]
  We propose a novel discrete event prediction framework for new users. Our method offers an unbiased prediction for new users without needing to know their categories. We demonstrate the superior performance of the proposed framework with a numerical simulation study and two real-world applications.
  arXiv  Detail & Related papers  (2024-07-08T05:35:54Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition [99.7047087527422]
  In this work, we demonstrate that competition can fundamentally alter the behavior of machine learning scaling trends. We find many settings where improving data representation quality decreases the overall predictive accuracy across users. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare.
  arXiv  Detail & Related papers  (2023-06-26T13:06:34Z)
• Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling [66.02953670238647]
  Tail users suffer from significantly lower-quality recommendation than the head users after joint training. A model trained on tail users separately still achieve inferior results due to limited data. We propose a novel approach that significantly improves the recommendation performance of the tail users.
  arXiv  Detail & Related papers  (2022-08-19T02:50:19Z)
• In the Eye of the Beholder: Robust Prediction with Causal User Modeling [27.294341513692164]
  We propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment.
  arXiv  Detail & Related papers  (2022-06-01T11:33:57Z)
• PinnerFormer: Sequence Modeling for User Representation at Pinterest [60.335384724891746]
  We introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action.
  arXiv  Detail & Related papers  (2022-05-09T18:26:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.