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【明理讲堂2023年第100期】12-22北京大学王聪助理教授: Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Pers

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


报告人:北京大学 王聪 助理教授




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.