通过校准与上下文感知，缓解地点推荐中的热门偏差问题。
Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
- 结合上下文信息与热度校准策略优化推荐
- 校准能有效匹配用户偏好，减少热门地点垄断
- 二者结合可平衡精度与公平性，适合注重多样性的系统
基于地点的推荐系统常因热门地点过度推荐而产生偏好偏差，导致冷门但有意义的地点被忽视。本文在四个真实数据集（Brightkite、Foursquare、Gowalla、Yelp）上评估了上下文感知模型与热度校准技术对缓解该偏差的效果。结果表明，不同上下文模型对准确率和偏差影响不一，无法统一适用；而热度校准若控制得当，可有效使推荐热度贴近用户真实偏好。关键发现是：校准与上下文感知结合后，推荐结果在准确率与用户热度偏好一致性之间达到良好平衡，实现‘热度校准’目标。
原文摘要 · Abstract (English)
Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popular, yet potentially meaningful places. This paper addresses this challenge by evaluating the effectiveness of context-aware models and calibrated popularity techniques as strategies for mitigating popularity bias. Using four real-world POI datasets (Brightkite, Foursquare, Gowalla, and Yelp), we analyze the individual and combined effects of these approaches on recommendation accuracy and popularity bias. Our results reveal that context-aware models cannot be considered a uniform solution, as the models studied exhibit divergent impacts on accuracy and bias. In contrast, calibration techniques can effectively align recommendation popularity with user preferences, provided there is a careful balance between accuracy and bias mitigation. Notably, the combination of calibration and context-awareness yields recommendations that balance accuracy and close alignment with the users' popularity profiles, i.e., popularity calibration.
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