Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games

TL;DR

This paper investigates bias in mobile game recommender systems, reviews existing debiasing techniques, and evaluates their effectiveness on real-world implicit feedback data to improve recommendation fairness.

Contribution

It provides a comprehensive analysis of bias types in mobile gaming data and assesses the performance of various debiasing methods in this specific context.

Findings

Debiasing techniques vary in effectiveness and computational cost.

Implicit feedback data presents unique challenges for bias mitigation.

Certain methods significantly improve recommendation fairness in mobile games.

Abstract

The mobile gaming industry, particularly the free-to-play sector, has been around for more than a decade, yet it still experiences rapid growth. The concept of games-as-service requires game developers to pay much more attention to recommendations of content in their games. With recommender systems (RS), the inevitable problem of bias in the data comes hand in hand. A lot of research has been done on the case of bias in RS for online retail or services, but much less is available for the specific case of the game industry. Also, in previous works, various debiasing techniques were tested on explicit feedback datasets, while it is much more common in mobile gaming data to only have implicit feedback. This case study aims to identify and categorize potential bias within datasets specific to model-based recommendations in mobile games, review debiasing techniques in the existing literature, and assess their effectiveness on real-world data gathered through implicit feedback. The effectiveness of these methods is then evaluated based on their debiasing quality, data requirements, and computational demands.