报告题目：Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Personalized Recommendations

时间：2023年12月22日 13：00-15：00

会议室：中关村校区主楼418会议室

报告人：北京大学 王聪 助理教授

报告人简介：

王聪，北京大学光华管理学院管理科学与信息系统系助理教授、博士生导师。于清华大学取得管理学博士学位，于北京大学取得管理学、经济学双学士学位，曾在卡耐基梅隆大学从事博士后研究工作。学术研究聚焦机器学习、数据挖掘等技术方法与管理问题的交叉点，目前主要关注于电子商务、金融科技、数据流通等领域的决策支持方法研究。研究成果曾发表于国内外知名学术期刊。

报告内容简介：

The abundance of multiple types of consumer digital footprints recorded on e-commerce platforms has fueled the design of personalized recommender systems. However, capturing consumers’ inherent preferences for effective recommendations based on consumer digital footprints can be challenging due to the multitude of factors driving consumer behaviors. Model training and recommendation outcomes may become biased if other factors are inappropriately recognized as consumers’ inherent preferences in the learning process. Drawing on consumer behavior theories, we tease out various factors that drive consumers’ digital footprints at different consumption stages. We develop a novel recommendation approach, namely DISC, which leverages disentangled representation learning with a causal graph to derive the effect of each factor driving consumer behaviors. This approach provides personalized and interpretable recommendations based on the inference of consumers’ normative inherent preferences. The DISC model’s identifiability is demonstrated through theoretical analysis, enabling rigorous causal inference based on observational data. To evaluate DISC’s performance, extensive experiments are conducted on two real-world data sets with a carefully designed protocol. The results demonstrate that DISC outperforms state-of-the-art baselines significantly and possesses good interpretability. Moreover, we illustrate the potential impact of different marketing strategies’ by intervening on the disentangled causes through follow-up counterfactual analyses based on the causal graph. Our study contributes to the literature and practice by causally unpacking the behavioral mechanism behind consumers’ digital footprints and designing an interpretable personalized recommendation approach anchored in their inherent preferences.

（承办：管理科学与物流系、科研与学术交流中心）