Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games
It addresses bias in recommender systems for mobile game developers, but is incremental as it applies existing techniques to a new domain.
This case study tackled bias in recommender systems for mobile games by reviewing and assessing debiasing techniques on real-world implicit feedback data, finding that some methods effectively reduced bias with moderate computational demands.
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.