# Large-scale User Game Lifecycle Representation Learning

arXiv: https://arxiv.org/abs/2510.15412 · HTML: https://mlsanity.com/paper/2510.15412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems](https://mlsanity.com/paper/2205.08084/) (2022)
- [Behavior Sequence Transformer for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1905.06874/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Item2Vec: Neural Item Embedding for Collaborative Filtering](https://mlsanity.com/paper/1603.04259/) (2016)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [Long-tail Session-based Recommendation](https://mlsanity.com/paper/2007.12329/) (2020)
- [MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation](https://mlsanity.com/paper/2308.11175/) (2023)
- [Zero-Shot Recommendation as Language Modeling](https://mlsanity.com/paper/2112.04184/) (2021)
- [PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision](https://mlsanity.com/paper/2010.01494/) (2020)
- [ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation](https://mlsanity.com/paper/2209.00456/) (2022)
- [Exploiting Behavioral Consistence for Universal User Representation](https://mlsanity.com/paper/2012.06146/) (2020)
- [Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling](https://mlsanity.com/paper/2010.11401/) (2020)
- [Attentive Item2Vec: Neural Attentive User Representations](https://mlsanity.com/paper/2002.06205/) (2020)
- [Instant Representation Learning for Recommendation over Large Dynamic Graphs](https://mlsanity.com/paper/2305.18622/) (2023)
- [A Machine-Learning Item Recommendation System for Video Games](https://mlsanity.com/paper/1806.04900/) (2018)
- [Sequential Recommendation with Causal Behavior Discovery](https://mlsanity.com/paper/2204.00216/) (2022)
- [Group Buying Recommendation Model Based on Multi-task Learning](https://mlsanity.com/paper/2211.14247/) (2022)

## Abstract (arXiv)

The rapid expansion of video game production necessitates the development of effective advertising and recommendation systems for online game platforms. Recommending and advertising games to users hinges on capturing their interest in games. However, existing representation learning methods crafted for handling billions of items in recommendation systems are unsuitable for game advertising and recommendation. This is primarily due to game sparsity, where the mere hundreds of games fall short for large-scale user representation learning, and game imbalance, where user behaviors are overwhelmingly dominated by a handful of popular games. To address the sparsity issue, we introduce the User Game Lifecycle (UGL), designed to enrich user behaviors in games. Additionally, we propose two innovative strategies aimed at manipulating user behaviors to more effectively extract both short and long-term interests. To tackle the game imbalance challenge, we present an Inverse Probability Masking strategy for UGL representation learning. The offline and online experimental results demonstrate that the UGL representations significantly enhance model by achieving a 1.83% AUC offline increase on average and a 21.67% CVR online increase on average for game advertising and a 0.5% AUC offline increase and a 0.82% ARPU online increase for in-game item recommendation.