{"ID":714134,"CreatedAt":"2026-03-04T20:59:41Z","UpdatedAt":"2026-03-04T20:59:41Z","DeletedAt":null,"paper_url":"https://paperswithcode.com/paper/shore-a-long-term-user-lifetime-value","arxiv_id":"2506.10487","title":"SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games","abstract":"In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-value outliers. While existing models typically rely on either short-cycle observations or strong distributional assumptions, such approaches often underestimate long-term value or suffer from poor robustness. To address these issues, we propose SHort-cycle auxiliary with Order-preserving REgression (SHORE), a novel LTV prediction framework that integrates short-horizon predictions (e.g., LTV-15 and LTV-30) as auxiliary tasks to enhance long-cycle targets (e.g., LTV-60). SHORE also introduces a hybrid loss function combining order-preserving multi-class classification and a dynamic Huber loss to mitigate the influence of zero-inflation and outlier payment behavior. Extensive offline and online experiments on real-world datasets demonstrate that SHORE significantly outperforms existing baselines, achieving a 47.91\\% relative reduction in prediction error in online deployment. These results highlight SHORE's practical effectiveness and robustness in industrial-scale LTV prediction for digital games.","url_abs":"https://arxiv.org/abs/2506.10487v1","url_pdf":"https://arxiv.org/pdf/2506.10487v1.pdf","authors":"[\"Shuaiqi Sun\", \"Congde Yuan\", \"Haoqiang Yang\", \"Mengzhuo Guo\", \"Guiying Wei\", \"Jiangbo Tian\"]","published":"2025-06-12T00:00:00Z","tasks":"[\"Multi-class Classification\", \"Prediction\", \"Value prediction\"]","methods":"[\"Huber loss\"]","has_code":false}