報告題目：Relevance or Profits? Cost-Aware Recommender System Design for Streaming Services

報告時間：周四（12月24日）上午10:00

報告地點：旭日樓306

報告人：姜正瑞 教授

報告人簡介：

姜正瑞教授是南京大學商學院營銷與電子商務系教授，二級教授，博士生導師。在2019年加入南大之前，任美國愛荷華州立大學的信息系統與商業分析教授和托米講席教授。主要研究領域是商務智能與大數據分析，其研究特色是将計算機科學研究與管理學研究有效融合，在商業數據分析、機器學習、決策支持和科技創新擴散等方向做出了重要貢獻，在國際頂級期刊如 Management Science, MIS Quarterly, Information Systems Research, IEEE Transactions on Knowledge and Data Engineering 等發表十多篇論文。其研究成果還被應用于企業、非盈利組織和政府的實踐，取得了客觀的經濟效益。現為國際頂尖期刊Information Systems Research 的副主編和Production and Operations Management 的高級主編；曾任另一頂級期刊MIS Quarterly 的副主編，并獲得該刊2016年最佳副主編獎。主持過信息系統領域多個國際性的學術會議。作為項目主持人曾收到國家自然科學基金和美國國際開發署的資助，在北美、中國和非洲從事過科研和知識傳播的工作。2019年被南京市委市政府授予“南京市高層次舉薦人才（A類）”的榮譽稱号。

報告簡介：

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.