Summary of Development Of An End-to-end Machine Learning System with Application to In-app Purchases, by Dionysios Varelas et al.
Development of an End-to-end Machine Learning System with Application to In-app Purchases
by Dionysios Varelas, Elena Bonan, Lewis Anderson, Anders Englesson, Christoffer Åhrling, Adrian Chmielewski-Anders
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning systems have revolutionized the mobile gaming industry by optimizing various aspects of gameplay. One crucial area is in-app purchases, which allow players to enhance their experience. This paper presents an ML-based approach to predict when a player will make their next in-app purchase, enabling targeted offers. The system leverages [model name], [method], and [dataset] to achieve [performance metric] on the [benchmark dataset]. By developing this predictive model, companies like King can improve their customers’ engagement and revenue. This work demonstrates the potential of ML in mobile gaming, with applications extending beyond in-app purchases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping game developers make better games. In mobile games, players buy things to make their gameplay more fun. Game makers want to know when a player will buy something again so they can offer them good deals. This paper explains how they did this using special computer programs and data from the game. They developed an ML system that predicts when someone will buy something next. This helps game developers give players better offers, making the gaming experience more fun and profitable. |
Keywords
» Artificial intelligence » Machine learning
