针对手游推荐中的隐式反馈数据偏见，评估去偏技术效果。
Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games
- 分析手游隐式反馈数据中的潜在偏见类型。
- 在真实数据上测试多种去偏技术的有效性。
- 适合关注游戏推荐系统公平性的研究者与开发者。
移动游戏行业，尤其是免费游玩模式，已发展十余年，仍持续快速增长。游戏即服务（Games-as-a-Service）模式要求开发者更加重视游戏内内容的推荐。推荐系统（RS）不可避免地面临数据偏见问题。尽管已有大量研究关注电商或在线服务中的推荐偏见，但针对游戏行业的研究仍较少。此外，以往工作多在显式反馈数据集上测试去偏技术，而移动游戏数据通常仅有隐式反馈。本案例研究旨在识别并分类模型推荐中手游数据集的潜在偏见，回顾现有文献中的去偏技术，并评估其在真实世界隐式反馈数据上的有效性。评估依据包括去偏质量、数据需求和计算开销。
原文摘要 · Abstract (English)
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.
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