  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.
