Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming

• URL: http://arxiv.org/abs/2409.17077v1
• Date: Wed, 25 Sep 2024 16:40:51 GMT
• Title: Efficient Feature Interactions with Transformers: Improving User Spending Propensity Predictions in Gaming
• Authors: Ved Prakash, Kartavya Kothari,
• Abstract summary: 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.
• Score: 0.5755004576310334
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Dream11 is a fantasy sports platform that allows users to create their own virtual teams for real-life sports events. We host multiple sports and matches for our 200M+ user base. In this RMG (real money gaming) setting, users pay an entry amount to participate in various contest products that we provide to users. In our current work, 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 aim to model the spending propensity of each user based on past transaction data. In this paper, we benchmark tree-based and deep-learning models that show good results on structured data, and we propose a new architecture change that is specifically designed to capture the rich interactions among the input features. 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. Our new transformer model surpasses the state-of-the-art FT-Transformer, improving MAE by 2.5\% and MSE by 21.8\%.

Related papers

• Mixture of Nested Experts: Adaptive Processing of Visual Tokens [49.43920770789789]
  Vision Transformer (ViT) based models fail to capitalize on inherent redundancy, leading to higher computational costs. We present Mixture of Nested Experts (MoNE), which utilizes a nested structure for experts, wherein individual experts fall on an increasing compute-accuracy curve. We validate our approach on standard image and video datasets - ImageNet-21K, Kinetics400, and Something-Something-v2.
  arXiv  Detail & Related papers  (2024-07-29T13:19:31Z)
• Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty [49.431361908465036]
  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.
  arXiv  Detail & Related papers  (2024-04-12T07:47:02Z)
• Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce [21.7796124109]
  We propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. AW-MoE has been successfully deployed in the JD e-commerce search engine.
  arXiv  Detail & Related papers  (2023-06-08T07:59:08Z)
• Predicting Customer Lifetime Value in Free-to-Play Games [0.0]
  We will present an overview of customer lifetime value modeling across different fields. We will introduce the challenges specific to free-to-play games across different platforms and genres.
  arXiv  Detail & Related papers  (2022-09-06T15:02:14Z)
• 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)
• User-click Modelling for Predicting Purchase Intent [0.0]
  This thesis contributes a structured inquiry into the open actuarial mathematics problem of modelling user behaviour. It is valuable for a company to understand user interactions with their website as it provides rich and individualized insight into consumer behaviour.
  arXiv  Detail & Related papers  (2021-12-03T16:37:48Z)
• Linear Speedup in Personalized Collaborative Learning [69.45124829480106]
  Personalization in federated learning can improve the accuracy of a model for a user by trading off the model's bias. We formalize the personalized collaborative learning problem as optimization of a user's objective. We explore conditions under which we can optimally trade-off their bias for a reduction in variance.
  arXiv  Detail & Related papers  (2021-11-10T22:12:52Z)
• PreSizE: Predicting Size in E-Commerce using Transformers [76.33790223551074]
  PreSizE is a novel deep learning framework which utilizes Transformers for accurate size prediction. We demonstrate that PreSizE is capable of achieving superior prediction performance compared to previous state-of-the-art baselines. As a proof of concept, we demonstrate that size predictions made by PreSizE can be effectively integrated into an existing production recommender system.
  arXiv  Detail & Related papers  (2021-05-04T15:23:59Z)
• Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data [63.942632088208505]
  Super-Apps have changed the way we think about the interactions between users and commerce. This paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior.
  arXiv  Detail & Related papers  (2021-02-19T15:13:06Z)
• TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning [36.38658213969406]
  We propose a novel user intent prediction model, TPG-DNN, to complete the challenging task. The proposed model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform.
  arXiv  Detail & Related papers  (2020-08-05T13:25:53Z)

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.