深入审视电影推荐系统中的性别刻板印象及其隐私影响
A Closer Look on Gender Stereotypes in Movie Recommender Systems and Their Implications with Privacy
AI总结：
本文通过630人用户研究与四种推理算法，结合MovieLens 1M和Yahoo!Movie数据集，揭示电影推荐中的性别刻板印象可被用于隐私推断。
摘要：
电影推荐系统通常利用用户反馈来提供符合用户偏好的个性化推荐，并增加业务收入。本研究通过一个特定攻击场景调查性别刻板印象对此类系统的影响。在该场景中，攻击者利用关于电影偏好的性别刻板印象，并分析公开可得或在系统内观察到的用户反馈数据，从而确定用户的性别这一隐私属性。研究分为两个阶段。第一阶段开展了一项涉及630名参与者的用户研究，识别出与电影类型相关的性别刻板印象，而这些刻板印象往往会影响观看选择。第二阶段应用四种推理算法，将第一阶段的发现与用户反馈数据相结合，以检测性别刻板印象。结果表明，这些算法在性别推理方面比仅依赖反馈数据表现更有效。此外，我们量化了性别刻板印象的程度，以评估其对数字计算科学的更广泛影响。研究的后半部分使用了两个主要的电影推荐数据集：MovieLens 1M和Yahoo!Movie。详细实验信息可在我们的GitHub仓库获取：https://github.com/fr-iit/GSMRS
英文摘要：
The movie recommender system typically leverages user feedback to provide personalized recommendations that align with user preferences and increase business revenue. This study investigates the impact of gender stereotypes on such systems through a specific attack scenario. In this scenario, an attacker determines users' gender, a private attribute, by exploiting gender stereotypes about movie preferences and analyzing users' feedback data, which is either publicly available or observed within the system. The study consists of two phases. In the first phase, a user study involving 630 participants identified gender stereotypes associated with movie genres, which often influence viewing choices. In the second phase, four inference algorithms were applied to detect gender stereotypes by combining the findings from the first phase with users' feedback data. Results showed that these algorithms performed more effectively than relying solely on feedback data for gender inference. Additionally, we quantified the extent of gender stereotypes to evaluate their broader impact on digital computational science. The latter part of the study utilized two major movie recommender datasets: MovieLens 1M and Yahoo!Movie. Detailed experimental information is available on our GitHub repository: https://github.com/fr-iit/GSMRS
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
