Summary of Addressing Bias in Recommender Systems: a Case Study on Data Debiasing Techniques in Mobile Games, by Yixiong Wang et al.
Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games
by Yixiong Wang, Maria Paskevich, Hui Wang
First submitted to arxiv on: 27 Nov 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 This research paper explores the issue of bias in recommender systems for mobile games, specifically in model-based recommendations. The study aims to identify and categorize potential biases in datasets used for mobile gaming, review existing debiasing techniques, and assess their effectiveness on real-world data gathered through implicit feedback. The methods are evaluated based on their debiasing quality, data requirements, and computational demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In the world of mobile gaming, game developers need to make sure players are getting the right content recommendations. This is a big problem because people can be biased in what they like or dislike. Many studies have looked at bias in recommender systems for things like shopping or services, but not as much for games. The researchers in this study want to see if these same biases happen in mobile gaming and how we can fix them. |
