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

Published 12 Apr 2024 in cs.IR and cs.LG | (2404.08301v1)

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.

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References (54)
  1. 2023a. https://en.wikipedia.org/wiki/Coefficient_of_determination
  2. 2023b. https://en.wikipedia.org/wiki/Coefficient_of_variation
  3. Neural news recommendation with long-and short-term user representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 336–345.
  4. CAN: feature co-action network for click-through rate prediction. In Proceedings of the fifteenth ACM international conference on web search and data mining. 57–65.
  5. Customer lifetime value prediction using embeddings. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1753–1762.
  6. Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 27–34.
  7. Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.
  8. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7–10.
  9. Andrew SC Ehrenberg. 1959. The pattern of consumer purchases. Journal of the Royal Statistical Society Series C: Applied Statistics 8, 1 (1959), 26–41.
  10. RFM and CLV: Using iso-value curves for customer base analysis. Journal of marketing research 42, 4 (2005), 415–430.
  11. Customer-base analysis in a discrete-time noncontractual setting. Marketing Science 29, 6 (2010), 1086–1108.
  12. Major Greenwood and G Udny Yule. 1920. An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal statistical society 83, 2 (1920), 255–279.
  13. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
  14. Modeling customer lifetime value. Journal of service research 9, 2 (2006), 139–155.
  15. Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 355–364.
  16. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648.
  17. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173–182.
  18. Supervised contrastive learning. Advances in neural information processing systems 33 (2020), 18661–18673.
  19. Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 426–434.
  20. Contrastive representation learning: A framework and review. Ieee Access 8 (2020), 193907–193934.
  21. Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3243–3251.
  22. xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1754–1763.
  23. Edward C Malthouse and Robert C Blattberg. 2005. Can we predict customer lifetime value? Journal of interactive marketing 19, 1 (2005), 2–16.
  24. SimpleX: A simple and strong baseline for collaborative filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1243–1252.
  25. UltraGCN: ultra simplification of graph convolutional networks for recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1253–1262.
  26. Andriy Mnih and Russ R Salakhutdinov. 2007. Probabilistic matrix factorization. Advances in neural information processing systems 20 (2007).
  27. Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th international conference on data mining. IEEE, 497–506.
  28. Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995–1000.
  29. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
  30. Remi M Sakia. 1992. The Box-Cox transformation technique: a review. Journal of the Royal Statistical Society Series D: The Statistician 41, 2 (1992), 169–178.
  31. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285–295.
  32. Counting your customers: Who-are they and what will they do next? Management science 33, 1 (1987), 1–24.
  33. Customer lifetime value prediction in non-contractual freemium settings: Chasing high-value users using deep neural networks and SMOTE. (2018).
  34. Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM international conference on information and knowledge management. 1161–1170.
  35. Attentive recurrent social recommendation. In The 41st international ACM SIGIR conference on research & development in information retrieval. 185–194.
  36. Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering. IEEE Transactions on Knowledge and Data Engineering (2023).
  37. Arturo Valdivia. 2021. Customer lifetime value in mobile games: a note on stylized facts and statistical challenges. In 2021 IEEE Conference on Games (CoG). IEEE, 1–5.
  38. An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 293–302.
  39. Rajkumar Venkatesan and Vita Kumar. 2004. A customer lifetime value framework for customer selection and resource allocation strategy. Journal of marketing 68, 4 (2004), 106–125.
  40. Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17. 1–7.
  41. Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proceedings of the web conference 2021. 1785–1797.
  42. A deep probabilistic model for customer lifetime value prediction. arXiv preprint arXiv:1912.07753 (2019).
  43. Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2456–2466.
  44. Than Than Win and Khin Sundee Bo. 2020. Predicting customer class using customer lifetime value with random forest algorithm. In 2020 International Conference on Advanced Information Technologies (ICAIT). IEEE, 236–241.
  45. Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System Objectives. In Proceedings of the 17th ACM Conference on Recommender Systems. 1293–1294.
  46. Neural news recommendation with multi-head self-attention. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). 6389–6394.
  47. Hi-Transformer: hierarchical interactive transformer for efficient and effective long document modeling. arXiv preprint arXiv:2106.01040 (2021).
  48. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726–735.
  49. Diffnet++: A neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering 34, 10 (2020), 4753–4766.
  50. A neural influence diffusion model for social recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. 235–244.
  51. Learning Reliable User Representations from Volatile and Sparse Data to Accurately Predict Customer Lifetime Value. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3806–3816.
  52. CFFNN: Cross feature fusion neural network for collaborative filtering. IEEE Transactions on Knowledge and Data Engineering 34, 10 (2021), 4650–4662.
  53. Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3206–3215.
  54. perCLTV: A general system for personalized customer lifetime value prediction in online games. ACM Transactions on Information Systems 41, 1 (2023), 1–29.
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