CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems

推荐产品、地点和服务的系统在日常生活和商业中越来越常见，因此了解推荐算法如何影响个人用户和更大的社会群体的结果变得非常重要。 为此，项目团队将开发基于大规模历史数据集模拟用户行为的新方法。 这些方法将用于更好地理解训练数据集中的潜在偏见对构建和测试推荐系统的常用机器学习方法造成的脆弱性，以及描述常见评估指标的有效性，例如推荐准确性和多样性，给出人们在实践中如何与推荐系统交互的不同模型。 该团队将公开发布其数据集，软件和新的指标，以使其他研究人员和推荐系统开发人员受益。 这项工作还将为计算机科学课程材料的开发提供信息，这些材料涉及数据分析的社会影响以及图书馆员的外联活动，图书馆员通常处于帮助信息寻求者了解搜索引擎和其他推荐系统如何影响他们获得所需信息的能力的位置。 第一个将量化和减轻离线推荐评估中的流行偏见和错误分类诱饵问题，这些问题往往会导致流行的，已知的推荐。 为此，该团队将开发基于模拟的评估模型，该模型编码了关于用户如何选择购买和评级的相关项目的各种假设，并使用它们来量化这些假设在推荐质量指标中引起的统计偏差。他们将通过与现有的书籍、研究论文、音乐和电影数据集进行比较来校准这些模拟。 这些模型和数据集将有助于推动第二个主要项目，即测量训练数据中的特征分布对推荐算法准确性和多样性的影响，同时开发抗偏见算法。 该团队将使用数据恢复技术沿着模拟模型，扩展到随着时间的推移对系统行为进行建模，以评估不同的算法如何通过其建议减轻，传播或加剧潜在的分布偏差，以及这些有偏见的建议如何反过来影响未来的用户行为和体验。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

Systems that recommend products, places, and services are an increasingly common part of everyday life and commerce, making it important to understand how recommendation algorithms affect outcomes for both individual users and larger social groups. To do this, the project team will develop novel methods of simulating users' behavior based on large-scale historical datasets. These methods will be used to better understand vulnerabilities that underlying biases in training datasets pose to commonly-used machine learning-based methods for building and testing recommender systems, as well as characterize the effectiveness of common evaluation metrics such as recommendation accuracy and diversity given different models of how people interact with recommender systems in practice. The team will publicly release its datasets, software, and novel metrics for the benefit of other researchers and developers of recommender systems. The work also will inform the development of computer science course materials about the social impact of data analytics as well as outreach activities for librarians, who are often in the position of helping information seekers understand the way search engines and other recommender systems affect their ability to get what they need.The work is organized around two main themes. The first will quantify and mitigate the popularity bias and misclassified decoy problems in offline recommender evaluation that tend to lead to popular, known recommendations. To do this, the team will develop simulation-based evaluation models that encode a variety of assumptions about how users select relevant items to buy and rate and use them to quantify the statistical biases these assumptions induce in recommendation quality metrics. They will calibrate these simulations by comparing with existing data sets covering books, research papers, music, and movies. These models and datasets will help drive the second main project around measuring the impact of feature distributions in training data on recommender algorithm accuracy and diversity, while developing bias-resistant algorithms. The team will use data resampling techniques along with the simulation models, extended to model system behavior over time, to evaluate how different algorithms mitigate, propagate, or exacerbate underlying distributional biases through their recommendations, and how those biased recommendations in turn affect future user behavior and experience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.