Mini-Game Lifetime Value Prediction in WeChat

TL;DR

This paper introduces GRePO-LTV, a novel framework combining graph representation learning and Pareto-Optimization to improve lifetime value prediction for WeChat mini-games, addressing data scarcity and task interdependence.

Contribution

The paper proposes an innovative framework that leverages graph learning and Pareto-Optimization to enhance LTV prediction accuracy under data scarcity and correlated tasks.

Findings

Improved LTV prediction accuracy demonstrated.

Effective handling of data scarcity issues.

Better management of correlated predictive tasks.

Abstract

The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV prediction system enhances the alignment of user interests with meticulously designed advertisements, thereby generating substantial profits for advertisers. Nonetheless, this issue is complicated by the paucity of data typically observed in real-world advertising scenarios. The purchase rate among registered users is often as critically low as 0.1%, resulting in a dataset where the majority of users make only several purchases. Consequently, there is insufficient supervisory signal for effectively training the LTV prediction model. An additional challenge emerges from the interdependencies among tasks with high correlation. It is a common practice to estimate a user's contribution to a game over a specified temporal interval. Varying the lengths of these intervals corresponds to distinct predictive tasks, which are highly correlated. For instance, predictions over a 7-day period are heavily reliant on forecasts made over a 3-day period, where exceptional cases can adversely affect the accuracy of both tasks. In order to comprehensively address the aforementioned challenges, we introduce an innovative framework denoted as Graph-Represented Pareto-Optimal LifeTime Value prediction (GRePO-LTV). Graph representation learning is initially employed to address the issue of data scarcity. Subsequently, Pareto-Optimization is utilized to manage the interdependence of prediction tasks.