報告題目：Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Personalized Recommendations
時間：2023年12月22日 13：00-15：00
會議室：中關(guān)村校區(qū)主樓418會議室
報告人：北京大學 王聰 助理教授
報告人簡介：
王聰，北京大學光華偉德國際1946bv官網(wǎng)管理科學與信息系統(tǒng)系助理教授、博士生導師。于清華大學取得管理學博士學位，于北京大學取得管理學、經(jīng)濟學雙學士學位，曾在卡耐基梅隆大學從事博士后研究工作。學術(shù)研究聚焦機器學習、數(shù)據(jù)挖掘等技術(shù)方法與管理問題的交叉點，目前主要關(guān)注于電子商務(wù)、金融科技、數(shù)據(jù)流通等領(lǐng)域的決策支持方法研究。研究成果曾發(fā)表于國內(nèi)外知名學術(shù)期刊。
報告內(nèi)容簡介：
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.
（承辦：管理科學與物流系、科研與學術(shù)交流中心）
