报告人简介：姜正瑞教授，南京大学商学院营销与电子商务系教授，二级教授，博士生导师。在2019年加入南大之前，任美国爱荷华州立大学的信息系统与商业分析教授和托米讲席教授。主要研究领域是商务智能与大数据分析，其研究特色是将计算机科学研究与管理学研究有效融合，在商业数据分析、机器学习、决策支持和科技创新扩散等方向做出了重要贡献，在国际顶级期刊如 Management Science, MIS Quarterly, Information Systems Research, IEEE Transactions on Knowledge and Data Engineering 等发表十多篇论文。其研究成果还被应用于企业、非盈利组织和政府的实践，取得了客观的经济效益。现为国际顶尖期刊Information Systems Research 的副主编和Production and Operations Management 的高级主编；曾任另一顶级期刊MIS Quarterly 的副主编，并获得该刊2016年最佳副主编奖。主持过信息系统领域多个国际性的学术会议。作为项目主持人曾收到国家自然科学基金和美国国际开发署的资助，在北美、中国和非洲从事过科研和知识传播的工作。2019年被南京市委市政府授予“南京市高层次举荐人才（A类）”的荣誉称号。

报告简介：This study proposes a cost-aware recommender system design that balances the relevance and cost of recommendations. The new design uses a control named cost-regularization effort to adjust the weight of cost in relation to relevance when recommending items to consumers. We investigate the cost-aware recommender system design in the context of digital streaming services. Our analytical results show that a streaming platform’s optimal cost-regularization effort increases with a subscriber’s streaming frequency and decreases with the subscription fee, implying that it is beneficial to recommend less relevant but less expensive contents to high-frequency subscribers or subscribers who pay a lower fee. Under the optimal cost regularization effort, when the maximum average utility per session is small, a subscriber’s derived utility decreases monotonically as the subscription fee increases; when the utility per session is sufficiently large, a subscriber’s derived utility first increases and then decreases as the subscription fee increases. We find that, under a discriminatory pricing policy, the optimal subscription fee charged to high-frequency subscribers should be higher than that to low-frequency subscribers, but the same cost-regularization effort should be applied to both subscriber segments. Compared to uniform pricing, discriminatory pricing improves the platform’s profit, and increases the surplus of at least one segments, but possibly both segments, of subscribers. These insights can help digital streaming platforms strategically personalize their recommendations to consumers to achieve a better long-term performance.