In online games, user profiling plays a vital role in a variety of personalized services. Current solutions typically treat different dimensions or labels (e.g., willing to pay or not, high, medium, or low appetite for some gameplays) of the full user profiles as independent multi-class/binary classification tasks. However, such one-by-one profiling strategy clearly overlooks the implicitly correlations among profiling tasks, which results in a degraded performance. To cope with this issue, we make the first attempt to formalize this problem as a multi-label learning task. Accordingly, we develop a unified Multi-Source Multi-Label learning framework~(MSML) that well utilizes semantically rich features and labels for boosted user profiling in online games. Specifically, we first introduce a multi-source user representation network that exploits multi-source data in online games to obtain informative user representations. Subsequently, to handle multiple labels, we propose a novel embedding-based multi-label network that consists of two variational autoencoders with disentangled latent spaces. Note that our framework can guarantee the consistency of the training and testing phases by a novel dual-tower design to overcome the limitation of existing approaches that use one coupled decoder for both features and labels. Extensive experiments on six public multi-label datasets and one real-world online game dataset from Justice demonstrate that the proposed framework outperforms the state-of-the-art baseline methods. Moreover, our proposed framework has been successfully deployed in several online games, yielding a significant boost in multi-label user profiling.

Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. Therefore, Customer Lifetime Value (CLTV) is particularly vital for game companies to improve marketing decisions and increase game revenues. Nowadays, as virtual game worlds are becoming increasingly innovative, complex, and diverse, the CLTV of massive players is highly personalized. That is, different players may have very different patterns of CLTV, especially on churn and payment. However, current solutions are inadequate in terms of personalization and thus limit predictive performance. First, most methods just attempt to address either task of CLTV, i.e., churn or payment, and only consider the personalization from one of them. Second, the correlation between churn and payment has not received enough attention and its personalization has not been fully explored yet. Last, most solutions around this line are conducted based on historical data where the evaluation is not convincing enough without real-world tests. To tackle these problems, we propose a general system to predict personalized customer lifetime value in online games, named perCLTV. To be specific, we revisit the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion. On this basis, we develop a generalized framework to model CLTV across games in distinct genres by heterogeneous player behavior data, including individual behavior sequential data and social behavior graph data. Comprehensive experiments on three real-world datasets validate the effectiveness and rationality of perCLTV, which significantly outperforms other baseline methods. Our work has been implemented and deployed in many online games released from NetEase Games. Online A/B testing in production shows that perCLTV achieves a prominent improvement in two precision marketing applications of popup recommendation and churn intervention.

As an independent social and economic entity, game servers plays a dominant role in building a stable, living, and attractive virtual world in massive multi-player online role-playing games (MMORPGs). We propose and implement a novel intelligent decision support system for server merge (SM) for maintaining the game ecology at the macro level. The services provided by this system include server health diagnosis, server merge assessment, and combination strategy recommendation. Specifically, we design an effective time series prediction algorithm to diagnose the health status of one server (e.g., user activity, online time, daily revenue) based on real game scenarios, and then select the servers with poor status from all servers. Moreover, to dig out the inherent development laws of servers from the historical merge records, we leverage a correlation measurement algorithm to find the historical merged servers that are similar to the servers to be merged and then evaluate the potential trend after merging, which can assist experts to make reasonable decisions. We deploy our system into practice for multiple MMORPGs and achieve sound online performance endorsed by the game operation team.

Profiling game players, especially potential churn and payment prediction, is of paramount importance for online games to improve the product design and the revenue. However, current solutions view either churn or payment prediction as an independent task and most of the previous attempts only depend on the single data source, i.e., the tabular portrait data. Based on the data of two real-world online games, we conduct extensive data analysis. On the one hand, there exists a significant correlation between the player churn and payment. On the other hand, heterogeneous multi-source data, including player portrait, behavior sequence, and social network, can complement each other for a better understanding of each player. To this end, we propose a novel Multi-source Data Multi-task Learning approach, named MSDMT, to capture the multi-source implicit information and predict the churn and payment of each player simultaneously in a multi-task learning fashion. Comprehensive experiments on two real-world datasets validate the effectiveness and rationality of our proposed method, which yields significant improvements against other baseline approaches.