Large-scale User Game Lifecycle Representation Learning

• URL: http://arxiv.org/abs/2510.15412v2
• Date: Mon, 20 Oct 2025 02:26:01 GMT
• Title: Large-scale User Game Lifecycle Representation Learning
• Authors: Yanjie Gou, Jiangming Liu, Kouying Xue, Yi Hu,
• Abstract summary: The rapid expansion of video game production necessitates the development of effective advertising and recommendation systems for online game platforms.<n>Existing representation learning methods crafted for handling billions of items in recommendation systems are unsuitable for game advertising and recommendation.<n>This is primarily due to game sparsity, where the mere hundreds of games fall short for large-scale user representation learning.
• Score: 12.075527204127091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents [56.25101378553328]
  We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned keyboard-mouse inputs.<n>Game-TARS is pre-trained on over 500B tokens with diverse trajectories and multimodal data.<n> Experiments show that Game-TARS achieves about 2 times the success rate over the previous sota model on open-world Minecraft tasks.
  arXiv  Detail & Related papers  (2025-10-27T17:43:51Z)
• SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning [27.20778530252474]
  SPIRAL is a self-play framework where models learn by playing multi-turn, zero-sum games against continuously improving versions of themselves.<n>Using SPIRAL, self-play on zero-sum games produces reasoning capabilities that transfer broadly.<n>Analysis reveals that this transfer occurs through three cognitive patterns: systematic decomposition, expected value calculation, and case-by-case analysis.
  arXiv  Detail & Related papers  (2025-06-30T17:58:13Z)
• Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play [52.3079697845254]
  eva is the first method that allows language models to adaptively create training prompts in both offline and online RL post-training.<n>We show eva can create effective RL curricula and is robust across ablations.
  arXiv  Detail & Related papers  (2024-10-31T08:15:32Z)
• 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)
• A Conceptual Design of In-Game Real and Virtual Currency Tracker [0.0]
  In-game virtual currencies can invoke inadequate gaming behaviors and additions among players. The market lacks gaming and customer protection regulations to avoid the financial, behavioral, and psychological exploitation of users. This paper presents a conceptual design of an in-game purchasing module that allows the user to observe their real time spendings in relation to virtual currency buying.
  arXiv  Detail & Related papers  (2024-04-05T08:34:22Z)
• COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation [33.903096803803706]
  We argue that a visual feature pre-training method tailored for recommendation is necessary for further improvements beyond existing modality features. We propose an effective user intention reconstruction module to mine visual features related to user interests from behavior histories.
  arXiv  Detail & Related papers  (2023-06-08T07:45:24Z)
• Sequential Item Recommendation in the MOBA Game Dota 2 [64.8963467704218]
  We explore the applicability of Sequential Item Recommendation (SIR) models in the context of purchase recommendations in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.
  arXiv  Detail & Related papers  (2022-01-17T14:19:17Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
  We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
  arXiv  Detail & Related papers  (2020-11-04T12:12:25Z)
• Deep Reinforcement Learning for FlipIt Security Game [2.0624765454705654]
  We describe a deep learning model in which agents adapt to different classes of opponents and learn the optimal counter-strategy. We apply our model to FlipIt, a two-player security game in which both players, the attacker and the defender, compete for ownership of a shared resource. Our model is a deep neural network combined with Q-learning and is trained to maximize the defender's time of ownership of the resource.
  arXiv  Detail & Related papers  (2020-02-28T18:26:24Z)
• Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning [82.25034245150582]
  In many vision-based reinforcement learning problems, the agent controls a movable object in its visual field. We propose an end-to-end learning framework to disentangle the controllable object from the observation signal. The disentangled representation is shown to be useful for RL as additional observation channels to the agent.
  arXiv  Detail & Related papers  (2020-02-21T05:43:34Z)

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.