Online game advertising is a prominent class of Web-mediated interactive services, where understanding and predicting player Lifetime Value (LTV) is a core scientific challenge in Web-scale user modeling, personalization, and digital economy optimization. However, the LTV prediction task poses severe challenges to traditional methods, which include data sparsity and complex distribution characteristics (such as zero-inflation, long tail, multimodal distribution, and cross-game). Existing methods struggle to capture the realistic and complex LTV distributions and exhibit limitations in leveraging cross-game data. We propose the first cross-game dynamic mixture framework with weighted sub-distributions for LTV prediction, DynaMoLTV. DynaMoLTV primarily models complex distributions via a zero-inflated mixture of lognormal (ZIMLN) loss, incorporates a game expert for cross-game data adaptation, employs a hierarchical payment classifier to capture consumption pattern variations, and integrates coarse and fine-grained losses to balance high-value user identification with LTV prediction accuracy. We conduct comprehensive experiments. The results demonstrate that DynaMoLTV achieves the best performance compared to five state-of-the-art baselines across metrics, including paid user identification, high-value user recall and LTV prediction accuracy. Specifically on three gaming datasets, DynaMoLTV reduces RMSE by 0.76%–46.65%, improves AUC by 0.94%–7.11%, and improves Norm-GINI by 0.63%–11.77% compared to five state-of-the-art baselines. DynaMoLTV also significantly improves ranking capabilities, with Recall@50K increasing by 17.64%–577.78%. We validate DynaMoLTV's effectiveness through two online A/B tests: (1) In the scenario of churned user re-engagement, DynaMoLTV increases online LTV by 20.3%-142.6% and downloads by 22.6%-37.7%. (2) In the scenario of online game advertising, DynaMoLTV increases GMV by 1.89% and GMV(ROI) by 27.31%. Our method has been fully deployed in a Web-based online game advertising platform, which ensures that LTV predictions remain personalized for online gaming ad delivery, supporting smarter and more inclusive decision-making on the Web.

As one of the most well-known programmer Q&A websites, Stack Overflow (i.e., SO) is serving tens of thousands of developers every day. Previous work has shown that many developers reuse the code snippets on SO when they find an answer (from SO) that functionally matches the programming problem they encounter in their development activities. To study how programmers reuse code on SO during project development, we conduct a comprehensive empirical study. First, to capture the development activities of programmers, we collect 342,148 modified code snippets in commits from 793 open-source Java projects, and these modified code can reflect the programming problems encountered during development. We also collect the code snippets from 1,355,617 posts on SO. Then, we employ CCFinder to detect the code clone between the modified code from commits and the code from SO, and further analyze the code reuse when programmer solves a programming problem during development. We count the code reuse ratios of the modified code snippets in the commits of each project in different years, the results show that the average code reuse ratio is 6.32%, and the maximum is 8.38%. The code reuse ratio in project commits has increased year by year, and the proportion of code reuse in the newly established project is higher than that of old projects. We also find that some projects reuse the code snippets from many years ago. Additionally, we find that experienced developers seem to be more likely to reuse the knowledge on SO. Moreover, we find that the code reuse ratio in bug-related commits (6.67%) is slightly higher than that of in non-bug-related commits (6.59%). Furthermore, we also find that the code reuse ratio (14.44%) in Java class files that have undergone multiple modifications is more than double the overall code reuse ratio (6.32%).