论文标题
性别中的性别：仔细查看上下文感知建议中的用户属性
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation
论文作者
论文摘要
本文根据推荐系统社区中当前的关注来研究用户属性：多样性，覆盖范围，校准和数据最小化。在利用侧面信息的传统上下文感知的推荐系统的实验中，我们表明用户属性并不总是改善建议。然后，我们证明用户属性可能会对多样性和覆盖率产生负面影响。最后，我们调查了从培训数据中````生存''''到推荐人产生的建议列表的信息量。这些信息是一个薄弱的信号，将来可能会被利用进行校准或作为隐私泄漏进一步研究。
This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation. Then, we demonstrate that user attributes can negatively impact diversity and coverage. Finally, we investigate the amount of information about users that ``survives'' from the training data into the recommendation lists produced by the recommender. This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak.
